The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.
Sentiment Features based Analysis of Online Reviewsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Sentiment Features based Analysis of Online Reviewsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Using NLP Approach for Analyzing Customer Reviews cscpconf
The Web considers one of the main sources of customer opinions and reviews which they are
represented in two formats; structured data (numeric ratings) and unstructured data (textual
comments). Millions of textual comments about goods and services are posted on the web by
customers and every day thousands are added, make it a big challenge to read and understand
them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those
opinions and reviews. In this paper, we use natural language processing techniques to generate
some rules to help us understand customer opinions and reviews (textual comments) written in
the Arabic language for the purpose of understanding each one of them and then convert them
to a structured data. We use adjectives as a key point to highlight important information in the
text then we work around them to tag attributes that describe the subject of the reviews, and we
associate them with their values (adjectives).
An evolutionary optimization method for selecting features for speech emotion...TELKOMNIKA JOURNAL
Human-computer interactions benefit greatly from emotion recognition from speech. To promote a contact-free environment in this coronavirus disease 2019 (COVID’19) pandemic situation, most digitally based systems used speech-based devices. Consequently, this emotion detection from speech has many beneficial applications for pathology. The vast majority of speech emotion recognition (SER) systems are designed based on machine learning or deep learning models. Therefore, need greater computing power and requirements. This issue was addressed by developing traditional algorithms for feature selection. Recent research has shown that nature-inspired or evolutionary algorithms such as equilibrium optimization (EO) and cuckoo search (CS) based meta-heuristic approaches are superior to the traditional feature selection (FS) models in terms of recognition performance. The purpose of this study is to investigate the impact of feature selection meta-heuristic approaches on emotion recognition from speech. To achieve this, we selected the rayerson audio-visual database of emotional speech and song (RAVDESS) database and obtained maximum recognition accuracy of 89.64% using the EO algorithm and 92.71% using the CS algorithm. For this final step, we plotted the associated precision and F1 score for each of the emotional classes
Opinion mining on newspaper headlines using SVM and NLPIJECEIAES
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.
Evaluating sentiment analysis and word embedding techniques on BrexitIAESIJAI
In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields collecting review from people about products and social and political events through the web. Currently, Sentiment Analysis concentrates for subjective statements or on subjectivity and overlook objective statements which carry sentiment(s). During the sentiment classification more challenging problem are faced due to the ambiguous sense of words, negation words and intensifier. Due to its importance the correct sense of target word is extracted and determined for which the similarity arise in WordNet Glosses. This paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field.
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
More Related Content
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USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Using NLP Approach for Analyzing Customer Reviews cscpconf
The Web considers one of the main sources of customer opinions and reviews which they are
represented in two formats; structured data (numeric ratings) and unstructured data (textual
comments). Millions of textual comments about goods and services are posted on the web by
customers and every day thousands are added, make it a big challenge to read and understand
them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those
opinions and reviews. In this paper, we use natural language processing techniques to generate
some rules to help us understand customer opinions and reviews (textual comments) written in
the Arabic language for the purpose of understanding each one of them and then convert them
to a structured data. We use adjectives as a key point to highlight important information in the
text then we work around them to tag attributes that describe the subject of the reviews, and we
associate them with their values (adjectives).
An evolutionary optimization method for selecting features for speech emotion...TELKOMNIKA JOURNAL
Human-computer interactions benefit greatly from emotion recognition from speech. To promote a contact-free environment in this coronavirus disease 2019 (COVID’19) pandemic situation, most digitally based systems used speech-based devices. Consequently, this emotion detection from speech has many beneficial applications for pathology. The vast majority of speech emotion recognition (SER) systems are designed based on machine learning or deep learning models. Therefore, need greater computing power and requirements. This issue was addressed by developing traditional algorithms for feature selection. Recent research has shown that nature-inspired or evolutionary algorithms such as equilibrium optimization (EO) and cuckoo search (CS) based meta-heuristic approaches are superior to the traditional feature selection (FS) models in terms of recognition performance. The purpose of this study is to investigate the impact of feature selection meta-heuristic approaches on emotion recognition from speech. To achieve this, we selected the rayerson audio-visual database of emotional speech and song (RAVDESS) database and obtained maximum recognition accuracy of 89.64% using the EO algorithm and 92.71% using the CS algorithm. For this final step, we plotted the associated precision and F1 score for each of the emotional classes
Opinion mining on newspaper headlines using SVM and NLPIJECEIAES
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.
Evaluating sentiment analysis and word embedding techniques on BrexitIAESIJAI
In this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields collecting review from people about products and social and political events through the web. Currently, Sentiment Analysis concentrates for subjective statements or on subjectivity and overlook objective statements which carry sentiment(s). During the sentiment classification more challenging problem are faced due to the ambiguous sense of words, negation words and intensifier. Due to its importance the correct sense of target word is extracted and determined for which the similarity arise in WordNet Glosses. This paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field.
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
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Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
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The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
About
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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Sentimental analysis of audio based customer reviews without textual conversion
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 14, No. 1, February 2024, pp. 653~661
ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp653-661 653
Journal homepage: http://ijece.iaescore.com
Sentimental analysis of audio based customer reviews without
textual conversion
Sumana Maradithaya1
, Anantshesh Katti2
1
Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India
2
Continental AG, Bengaluru, India
Article Info ABSTRACT
Article history:
Received May 23, 2023
Revised Jul 11, 2023
Accepted Jul 17, 2023
The current trends or procedures followed in the customer relation management
system (CRM) are based on reviews, mails, and other textual data, gathered in the
form of feedback from the customers. Sentiment analysis algorithms are deployed
in order to gain polarity results, which can be used to improve customer services.
But with evolving technologies, lately reviews or feedbacks are being dominated
by audio data. As per literature, the audio contents are being translated to text and
sentiments are analyzed using natural processing language techniques. However,
these approaches can be time consuming. The proposed work focuses on analyzing
the sentiments on the audio data itself without any textual conversion. The basic
sentiment analysis polarities are mostly termed as positive, negative, and natural.
But the focus is to make use of basic emotions as the base of deciding the polarity.
The proposed model uses deep neural network and features such as Mel frequency
cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based
reviews.
Keywords:
Deep neural network
Feature extraction
Mel frequency cepstral
coefficients
Natural language processing
Sentiment analysis
Speech emotion
This is an open access article under the CC BY-SA license.
Corresponding Author:
Sumana Maradithaya
Department of Information Science and Engineering, Ramaiah Institute of Technology
Bengaluru, India
Email: sumana.a.a@gmail.com
1. INTRODUCTION
Customer relation management system (CRM) is a system used by service providing companies to
gather reviews or feedbacks from their customers based on the products they bought. These reviews are in
reference to how the products or services were, How or what can be done in order to make the services or
products quality improve. These reviews or feedbacks collected are in terms of e-mails, messages or textual
form. The CRMs collect these reviews and feedbacks and they deploy sentiment analysis algorithms on them.
The algorithm is trained with some pre-defined labeled data by which the model learns. The traditional
sentiment analysis gives results in terms of three output namely, positive, negative and neutral known as
polarities. Thus, the collected feedback is fed through the trained model and the results are produced. Using
these results, the CRM informs the companies, the percentage of positive or negative or neutral polarities of
the collected feedbacks and based on the results, the company can decide on which directions or steps to take
in order to satisfy their customers best.
The sentiment analysis models are basically trained on millions of labeled text data where every
sentence is broken down to individual words using different pre-processing techniques. The basic analogy of
sentiment analysis can be given as, “I had a very pleasant day, today”. In this sentence, the word ‘pleasant’ is
a positive word and when we consider the combination of it with its previous word which is ‘very’, it becomes
a strong positive. Another example can be as, “The ocean is scary but without a doubt, beautiful”. In this
example, ‘scary’ is a negative word but there is also the word ‘beautiful’ which is a positive. Such a
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Int J Elec & Comp Eng, Vol. 14, No. 1, February 2024: 653-661
654
combination of negative and positive words can lead to any of the three outcomes when training the model. In
this statement, if only the first part is considered, then no doubt it is negative sentence. But if the entire sentence
is considered, the other half dominates and thus it can be termed as either positive or neutral sentence.
In the traditional approach to calculating the sentiment where textual data is being used, the customers
can express their concerns about the service or product only in terms of text. But with this, sometimes the
customers can easily lie in text feedbacks. Sometimes they can be sarcastic where they mean one thing, whereas
their words can mean a different thing. This can cause a problem for the companies and CRMs to provide with
the best outcomes. In order to avoid this gap, audio feedbacks can be used as input data. But converting the
audio data to text can lead to the same problem, so making use of audio data directly can result in better
outcomes. In this paper, the use of audio data directly to train the model as well as to predict the results is done.
Instead of just having positive, negative and neutral, this paper provides output in terms of neutral, calm, happy,
sad, angry, surprised, disgust. A labeled Ryerson audio-visual database of emotional speech and song
(RAVDESS) dataset is used to train the model.
Speech emotion is a key when it comes to paralinguistic element for communication, which without
a doubt has high subjectivity level. Sentimental varies among different languages and people [1] as the existing
work is focusing on investigating emotional states. The research has created an “emotional speech ground truth
database”, which consists of semantically and or emotionally “loaded” utterances speakers with five sentiments
polarity. Kim and Hansen [2] proposes “an effective angry speech detection approach” by considering structure
of the content of the input speech. A classifier which is focused on “emotional language” model for which the
score is formed and combined with acoustic feature that includes Teager energy operator (TEO) based feature
and Mel frequency cepstral co-efficients (MFCC). The proposed research has a 6.23% improvement in equal
error rate (EER) which is gained by combining the TEO-based and MFCC features. A brief comparison
between conversation sentiment analysis and single sentence sentiment analysis has been done in [3]. It
introduces a model called bidirectional emotional recurrent unit (BiERU) for conservation sentiment analysis.
This approach is focused on textual data and not speech or audio data. The BiERU uses a general neural tensor
model with 2 channel classifiers to analysis the context of the conversation and perform the sentiment analysis.
This model was compared with existing benchmark models like dialogue-recurrent neural networks
(dialogue-RNN), dialogue-convolutional neural networks (dialogue-CNN) and attention gated hierarchical
memory network (AGHMN) and the results in the proposed research outperform the existing models in terms
of accuracy between various sentiments. The accuracy of BiERU was found out to be a claimed average
accuracy of 66% and F1 score of 64% for textual conservational data. Garg and Sharma [4] discusses a process
of sentiment analysis using different representations on twitter data. In [5], authors have worked on a popular
word embedding approaches in identifying sentiments. Salehin et al. [6] discusses the use of support vector
machine (SVM) and OpenCV in analysing student sentiments in an online class. Further, Moung et al. [7] have
used an ensemble of methods in identifying sentiments in images. Novel approaches are required in finding
sentiments from audio data. Mahima et al. [8] discusses approaches in identifying multiple emotions from
textual data.
Bertero and Fung [9] proposes a real-time CNN approach for speech emotion detection. It is focused
on training with audio using a dataset from “TED talks”, which are then elucidated manually into three
emotions: “angry”, “happy” and “sad”. The research has achieved an accuracy averaging at 66.1%, which is
5% better than a feature based SVM baseline. Kantipud and Kumar [10] have proposed an interesting
computationally efficient learning model to classify audio signal attributes. Each filter is activated at multiple
frequencies, which is caused due to the amplitude-related feature learning. The proposed approach in Chen and
Luo [11] uses audio-based inputs by introducing utterance based deep neural network. This approach uses a
combination of convolutional neural networks (CNN) and audio sentiment vector (ASV). The process is based
on using utterance from the audio inputs to analysis the content and finds the spectrum graph generated from
the signals which are inputted to a long short-term memory (LSTM) model branch, making use of spectral
centroid, MFCC which are the traditional acoustic features. The proposed model has a claimed accuracy of
57.74%. Maghilnan and Kumar [12] proposed a model has been implemented using the audio sentiment
analysis as speaker discriminated speech data. The model has two starting points, one for speaker
discrimination and the other for speech recognition. In [13] the discussion is based on the sentiment analysis
for the customer relation management where the idea of have a customer loyal for a longer run after their first
purchase is very important for a company and their products. The proposed concept in [14] is to use multi-
aspect level sentiment analysis (MALSA) model in a CRM which not only helps find the sentiment but also
has a recommendation approach to recommend the customers throughout the purchase cycle. The model was
built on four discussions namely, Frequency based detection, Syntax based detection, supervised and
unsupervised learning and finally hybrid models for analyzing the sentiments. This model is entirely based on
textual data to find out the sentiments. Rotovei and Negru [15] is an extended study of [13] but with a slight
addition to the existing model where a component of B2B with recommendation is proposed. They have used
3. Int J Elec & Comp Eng ISSN: 2088-8708
Sentimental analysis of audio based customer reviews without textual conversion (Sumana Maradithaya)
655
aspect term extraction that has four approaches like, finding frequent nouns and noun phrases, use of opinion
and target relationships, supervised learning and topic modeling. And secondly uses aspect aggregation. In a
natural language sentence, there are lists of aspect that are produced from aspect based sentiment analysis [16].
The proposed research is based on interactive multi-task learning (IMU) that is implemented for tokens as well
as documents which are done simultaneously. A whole new algorithm has been proposed to train the IMU and
the model was compared with different other models and the proposed model outperforms the existing models
with a claimed accuracy of 83.89 and F1 score of 59.18.
The identification of sentiment polarity for specific targets in a context can be achieved by using
aspect-level sentiment classification [17]. The proposed research introduces an approach of using targets as
well as context where both are important and as well need special treatment which learns their representation
using the proposed interactive attention network (IAN). It can represent its collective context as well as targets.
The proposed model was compared with other existing models such as LSTM, temporal dependence-based
long short-term memory (TD-LSTM), autoencoder based LSTM (AE-LSTM) where IAN outperforms these
models by 4% to 5% with a claimed final accuracy of 72.1%. The dataset used in [18] is SemEval 2014 dataset.
In natural language processing, the fundamental task is aspect-level sentiment analysis [19] and the main aim
is to predict the polarity of sentiment of a given aspect term of a sentence. This research is similar to that of
research in [17] but with an improved and additional feature which addresses the drawback. In [19] the research
considers grammatical rules in an input sentence for sentiment analysis which was missing in previous
researches. The dataset used is the SemEval dataset and the proposed approach has a 2% more better
performing than the approach in [17] with a claimed accuracy of 74.89%. This proposed model was compared
with the other existing models such as LSTM, AE-LSTM, attention-based LSTM with aspect embedding
(ATAE-LSTM).
Sentiment analysis is not only limited to finding the sentiment straight forward from a textual sentence
but can also be used to find the lost-won classification of complex deals [14]. The model is built upon using
“Frequency, lexicon based and syntax-based detection”, “machine learning based approach” and “hybrid
methods”. The concept of using deep learning for the prediction of sentiment analysis from a given input, be it
a textual input data or a speech/audio input data. The same deep learning can be used for finding sentiments
from a given input data has been mentioned and extensive research has been carried out in [1]. The research
introduces all the possible approaches such as deep neural network, LSTM, auto encoders, word embedding,
CNN, RNN, attention mechanism with recurrent neural network document level sentiment analysis and other
few approaches. Zhang et al. [20] is an extensive research study of all the deep learning approaches that can
be used for sentiment analysis with all the possible features, characteristics and related implementation
approaches have been discussed. Sentiment analysis plays a crucial part in identifying customer gratification
and opinion. Capuano et al. [21] proposes a “hierarchical attention networks” for analyzing the sentiment
precedence of client’s feedbacks. The model can be seen for improvement using the reviews provided by CRM
which is possible by an “integrated incremental learning mechanism”. Capuano et al. [21] also focuses on a
prototype that has been developed and the dataset used for training with over large number of annotated items
has been used. The accuracy in the proposed research averaged 0.85 for the F1-score.
“Interactive sentiment analysis” in [22] is an emerging and a sub branch of the natural language
processing (NLP) problem. The implementation of new approaches is limited to the labeled datasets for
interactive sentiment. In [22], a new conversational database was created-Scenario SA. The dataset was
manually labeled. The increase in the use of smart mobile phones has enabled everyone to connect through
social media where chats, comments on posts and products can be seen. The sentiment analysis product reviews
entirely depend on lexicons. The generation of lexicons is a crucial thing that needs to be considered. In [23],
“automatic approach for constructing a domain-specific sentiment lexicon” has been proposed in view of words
consisting of sentiments and product feedbacks. The process chooses words consisting of sentiments from
reviews and focuses on the relation that uses common data algorithm. High-resolution or HR information from
social media Facebook or Quora presents great chance to CRM by analyzing arguments about commerce events
[24]. Textual sentiment analysis has been greatly developed and researched by using mainly lexicon-based
approaches. In [25] the research focuses on analyzing behavior of the customer for buying a product using the
product feedback. The proposed work introduced in [26] is of “multi-mixed short text ridge analysis
(MMSTR)” that is used to extract sentence and text feedbacks. The emotions are measured using the reviews.
The sentiments that are expressed by the customers are quite important; and hence a swift analysis is a must.
In [27], a “hierarchical approach” is put forward for sentiment analysis. Firstly, word embeddings of reviews
are analyzed by using Word2Vec. The research also considers binate sentiment analysis, i.e., resolving of
“positive” and “negative” as sentiments, an “extreme gradient boosting classifier” (xgboost) is used and on an
average feedback vector. An overall accuracy with 71.16% is gained that uses categorization of 12 different
classes that makes use of the Doc2Vec approach. Online shopping is growing day by day with popularity
among customers, with these product feedbacks and reviews are received through customers [28]. Online
analytical processing (OLAP) and Data Cubes techniques of data warehouse are used to analyze the sentences.
4. ISSN: 2088-8708
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The main challenges natural language processing are sentiment analysis and opinion mining [29]. The
work is based on methods used to identify text by which the opinions are considered i.e., “whether the overall
sentiment of an individual is negative or positive or neutral”. The research also considers two advanced
approaches along with experimental outcomes. By comparing three machines learning models for classification
techniques such as logistic regression, hybrid bag-boost algorithm and SVM are considered, and results are
analyzed. In [30], [31] discuss the various sentiments associated with reviews collected through twitter. The
polarities of the sentiments are identified as positive, negative or neutral.
In the implemented methodology, pre-processing pipeline for audio data as in [32] was implemented.
The pipeline consisted of loading, padding, feature extraction, normalization of audio data and finally saving
the outputs in the form of pikle extension. Features extraction techniques such as MFCC, chroma, Mel
spectrogram and features such as contrast and tonnetz are being extracted which are stored in the above said
pikle file format. Once it is done, the next step is to train the model with RAVDASS dataset. The implemented
model is based on a deep neural network for training from the dataset.
2. PROPOSED METHOD
Figure 1 shows the architecture of the model being implemented. The whole idea and mechanism start
with data being collected by the company’s department responsible for collecting feedbacks and reviews in the
form of audio recordings. Once all the data is being collected, it will be stored in a single storage location in
the format of .wav file extension. This stored data is then prepared by passing through some pre-processing
techniques such as loading all the audio files as batch, padding, extract features and finally save all the output
of these steps in a desired location. The features that are extracted are using the MFCCs technique and chroma,
Mel-spectrogram, contrast, tonnetz are the features that are directly extracted. All these features are saved as a
.pkl or pikle file in a desired location. This pikle file will be used as input to the implemented deep neural
network model for training. Once the training is completed, we can test out for a sample audio for prediction
of sentiment analysis. For which we need to extract the same set of features as done before and pass it to the
trained model for prediction. Based on this final prediction, the company can think of whether they need any
improvement in their service/product or not.
2.1. Audio pre-processing
For the audio pre-processing, an entire pipeline was implemented that included loading the audio files,
extracting features such as Mel spectrogram, MFCC technique, chroma, contrast, tonnetz. The results are then
stored in a pikel file. The loader class is responsible to load all the audio files from the mentioned directory in
batch. It is easier to work on batch files instead of loading each file individually and performing pre-processing
on them which can be difficult and also time consuming. The padder class in the audio pre-processing pipeline
is responsible to perform padding. The padder is used to add a zero-right padding to the audio files to match
one single length.
2.2. Feature extraction
Feature extraction on the audio data is essential to precisely identify the essential properties that assist
us to identify sentiments. The techniques used for features extractions are MFCC or Mel frequency cepstral
coefficient, Mel spectrogram, chroma, tonnetz and contrast. In the proposed work, this is considered in the final
implementation.
2.3. Mel frequency cepstral coefficient
MFCC is a major or most frequently used technique for extracting features from the audio signal such
as windowing the signal which is used to detect the phones in the audio. As there are plenty of phones in a
speech or an audio, the audios are broken down into smaller chunks of a said duration of 25 ms with 10 ms
apart from each chunk. The next is taking applying log. A human is sensitive to only a set of frequency and
cannot hear all types of frequency. In order to bridge this, log is applied so that the recorded audio can match
the human hearable frequency. The next one is preemphasis where the magnitude is increased in the high
frequency scale, performing discrete Fourier transform. The first order high pass filter is applied to pre-
emphasis as seen in (1) where x is any taken signal, with time ‘t’ and ‘α’ which has a constant value of 0.95.
The use of Mel-filter bank is done in MFCC as well, where the humans can distinguish between different
frequencies, it is not true for all set of frequency band. But the machine can differentiate with any frequency
band and this is done by using a formula as based on (2). It is used to convert Hz to Mel frequency scale. Here
‘f’ stands for frequency, 1,125 is the high frequency scale and 700 is low frequency scale.
𝑦(𝑡) = 𝑥(𝑡) − 𝘢 𝑥(𝑡 − 1) (1)
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𝑀𝑒𝑙(𝑓) = 1123 𝑙𝑜𝑔(1 +
𝑓
700
) (2)
MFCC technique comes pre-implemented with Librosa library and with the help of a dot operator; one can
call and use this technique’s algorithm to extract audio features and information. It also comes with analog to
digital conversion and the audio can also be sampled with a specific frequency like 8 or 16 kHz. Figure 2 shows
the visual representation of MFCC.
Figure 1. Architecture of audio sentiment analysis
Figure 2. MFCC of a sample audio
MFCC is a “visual representation of the short-term power spectrum of the audio signal which is based
on the linear cosine transform of a log power spectrum on the nonlinear Mel frequency scale”. Figure 3 shows
the Mel Spectrogram of a sample audio file. It is a representation of Mel-scale that is being converted from
spectrogram. A spectrogram is basically a representation of frequency spectrum of an audio signal where the
frequency spectrum is the frequency range of the audio signal which contains it. The bright color represents
high intensity and the darker color represents low intensity in the audio signal. Figure 4 is the Chroma
representation of a sample audio file. Chroma is basically a describes the representation of the tonal content in
the audio signal which is in the condensed form. In Chroma, the intensity is decided from bright to dark where
bright color represents low intensity and darker color represents high intensity.
Figure 5 shows the tonnetz representation of a sample audio file. Tonnetz is a concept representation
of lattice diagram which in-term is used to represent the tonal space. The y-axis is made of some alpha-numeric
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values. This is basically best explained by taking the example of a musical instrument. In any musical
instrument, there are some chords, which vary from instrument to instrument, like the guitar has chords like
Emajor, and E minor. The y-axis alpha-numeric values in Figure 5 are the examples of those chords. All these
features are shown in visual form which the machine or our model cannot understand. To make it possible, all
these features are converted to numeric representation from their respective form so that our machine can
understand which is done by using the Librosa library. Once these are converted to numeric form, they are then
stored in a pikle file.
Figure 3. Mel spectrogram of a sample audio
Figure 4. Chroma representation of sample audio
Figure 5. Tonnetz of sample audio file
3. RESULTS AND DISCUSSION
3.1. Training
The model was first trained with tanh as the activation function and rectified linear unit (ReLU) was the
immediate second choice. Numbers of preceptron were also being changed. The optimizer being used at first was
adadelta and later changing it to Adam. A few of these variations are being shown in Table 1. As the number of
hidden layers increased after three, the results were almost the same and there was no improvement in accuracy.
In order to maintain the efficiency of the model, it was best to use the least number of hidden layers and preceptron.
Thus, the final parameters used are shown in Table 2. The model is trained by using the RAVDASS dataset which
consists of about 1,400 audio files with each of them having 48 kHz and in wav format. The dataset is labeled
with different emotions such as neutral, calm, fearful, sad, happy, angry, disgust, surprised. Using features such
as MFCC, chroma, spectrogram, contrast, tonnetz and the labeled data, we pass these files through the neural
network. The end results being obtained as 95% training accuracy and 75% testing accuracy.
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Table 1. Model building iteration
Hidden layers Activation function Epoch Preceptrons Optimizer Dropout Testing accuracy Training accuracy
1 Tanh 50 100 Adadelta 0.5 14.58 20.45
2 Tanh 50 100, 200 Adadelta 0.5 24.31 18.13
2 ReLU 50 200, 300 Adam 0.2 46.53 50.38
2 ReLU 100 200, 300 Adam 0.1 48.61 66..28
3 ReLU 200 300, 400, 300 Adam 0.1 67.89 89.97
4 ReLU 200 600, 800, 800, 600 Adam 0.1 68.75 96.14
5 ReLU 200 600, 800, 800, 800, 600 Adam 0.1 67.36 92.36
Table 2. Final parameters used for model
Hidden layers Activation function Epoch Preceptrons Optimizer Dropout Testing accuracy Training accuracy
3 ReLU 200 400,600,400 Adam 0.1 75.09 95.49
3.2. Testing
A sample audio file with some spoken content can be inputted and the result can be predicted. But
first, feature extraction should be performed over this audio file and with the help of them; we can obtain the
result in terms of if it belongs to happy, sad, angry, neutral, surprised, calm categories. Table 2 shows the
implemented model is providing with over 95% training accuracy and when the model was put to test with a
test audio file, it performed with 75% accuracy. In [23] the results show about 89% accuracy, but this is because
the model was trained and tested after the conversion of audio data to contextual data for analysis andprediction.
The results are shown in Table 3.
𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑠𝑡𝑟 ((
𝑐𝑜𝑢𝑛𝑡
𝑦2.𝑠ℎ𝑎𝑝𝑒[0]
)) ∗ 100 (3)
As shown in (3) represents the formula used to calculate the accuracy where y2 contains the prediction of
model with respect to X_test and with a NumPy’s argmax value. Count is used to iterate over all the audio files
featurespresent in the pickle file. And shape is used to give the dimensions of the array. In case the test audio
file contains multiple speakers, then the need to identify the speakers and separate them in order to have only
the customer’s utterance in the test audio file. In order to do so the concept can be referred to and used from
[23]. Figure 6 represents the visual graph representation of the train and validation accuracies for the
implemented model against epochs of 200.
Table 3. Comparison with different model’s accuracies
Model Accuracy
XGBOOST 56%
Sentiment Analysis on Speaker Specific Speech Data [6] (Audio being converted to text) 89%
Proposed model 75%
Figure 6. Graph of train/test accuracies
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4. CONCLUSION
In the proposed model, the main focus is on the use of audio data for sentiment analysis, as most of
the feedbacks are being captured via voice and also to get better understanding of the customer’s reviews. In
orderto avoid customers using sarcastic comments to confuse the system, audio reviews work best to prevent
such things. The proposed model outperforms the existing systems or models by at least 10% in terms of
accuracy. For preprocessing of data, to avoid any analysis or preprocessing of audio files manually or one at a
time, a pipeline was proposed and implemented by which the entire preprocessing can be automated. This
pipeline can accommodate n number of methods as per the implementer’s requirements or needs. The
applications of using audio as inputs for sentiment analysis does not only apply for CRMs but this application
can be used medical domain for treatment of anxiety or depression.
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BIOGRAPHIES OF AUTHORS
Sumana Maradithaya is an associate professor at Ramaiah Institute of
Technology, India. She is a senior IEEE member and an IEEE-CIS member. She has paper
publications in reputable conferences and journals. She was actively involved in several IEEE
activities such as community outreach activities, industry interaction, webinars, and projects.
She has mentored industry related projects. She is an active reviewer for several reputed
journals and international IEEE (conecct and HPC) conferences. She has actively contributed
towards several projects in the areas of machine learning, data science and deep learning. She
can be contacted at email: sumana.a.a@gmail.com.
Anantshesh Katti has completed his M.Tech. in software engineering from
Ramaiah Institute of Technology, India. He is currently working as a Software Developer at
continental automotive working with C++, React and NodeJS Technologies. He has worked on
projects related to machine learning and deep learning. He also has freelancer’s experience in
phone applications development using Java and flutter technologies. He can be contacted at
email: anantsheshkatti@gmail.com.