This PPT include the project description of topic modelling to group reviews from Flipkart or Amazon. It contains the introduction, dataset used in the project, methodology or model used, result achieved and conclusion of the project.
Incentive Compatible Privacy Preserving Data Analysisrupasri mupparthi
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining.
In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Incentive Compatible Privacy Preserving Data Analysisrupasri mupparthi
Now a days, data management applications have evolved from pure storage and retrieval of information to finding interesting patterns and associations from large amounts of data. With the advancement of Internet and networking technologies, more and more computing applications, including data mining programs, are required to be conducted among multiple data sources that scattered around different spots, and to jointly conduct the computation to reach a common result. However, due to legal constraints and competition edges, privacy issues arise in the area of distributed data mining, thus leading to the interests from research community of both data mining.
In this project each party participates in a protocol to learn the output of some function f over the joint inputs of the parties. We mainly focus on the DNCC model instead of considering a probabilistic extension. Deterministic Non Cooperative Computation needs to be extended to include the possibility of collusion.
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
An overwhelming choice of applications, websites and digital platforms leaves our customers with multiple interaction channels and devices to connect with organizations. In a digitally connected economy, businesses need to represent a “single view” of the brand to the customer. The key here is to integrate customer information from multiple touch points and get a 360 degree view of the customer
This presentation talks about BRIDGEi2i’s Customer Experience Tracking Platform – ExTrack and how it could help businesses with near-real-time actionable recommendations for improving customer experience.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Detailed Investigation of Text Classification and Clustering of Twitter Data ...ijtsrd
As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima
Agile Mumbai 2022 - Rohit Handa | Combining Human and Artificial Intelligence...AgileNetwork
Agile Mumbai 2022
Combining Human and Artificial Intelligence for Business Agility
Rohit Handa
Director, Digital Products & Platforms, HCL Technologies Ltd
Framework for Product Recommandation for Review Datasetrahulmonikasharma
In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
Extracting Business Intelligence from Online Product Reviews ijsc
The project proposes to build a system which is capable of extracting business intelligence for a manufacturer, from online product reviews. For a particular product, it extracts a list of the discussed features and their associated sentiment scores. Online products reviews and review characteristics are extracted from www.Amazon.com. A two level filtering approach is adapted to choose a set of reviews that are perceived to be useful by customers. The filtering process is based on the concept that the reviewer generated textual content and other characteristics of the review, influence peer customers in making purchasing choices. The filtered reviews are then processed to obtain a relative sentiment score associated with each feature of the product that has been discussed in these reviews. Based on these scores, the customer's impression of each feature of the product can be judged and used for the manufacturers benefit.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Similar to Topic Modelling to Group Reviews from Flipkart
An overwhelming choice of applications, websites and digital platforms leaves our customers with multiple interaction channels and devices to connect with organizations. In a digitally connected economy, businesses need to represent a “single view” of the brand to the customer. The key here is to integrate customer information from multiple touch points and get a 360 degree view of the customer
This presentation talks about BRIDGEi2i’s Customer Experience Tracking Platform – ExTrack and how it could help businesses with near-real-time actionable recommendations for improving customer experience.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Detailed Investigation of Text Classification and Clustering of Twitter Data ...ijtsrd
As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima
Agile Mumbai 2022 - Rohit Handa | Combining Human and Artificial Intelligence...AgileNetwork
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In the social networking era, product reviews have a significant influence on the purchase decisions of customers so the market has recognized this problem The problem with this is that the customers do not know how these systems work which results in trust issues. Therefore a different system is needed that helps customers with their need to process the information in product reviews. There are different approaches and algorithms of data filtering and recommendation .Most existing recommender systems were developed for commercial domains with millions of users. In this paper we have discussed the recommendation system and its related research and implemented different techniques of the recommender system .
Extracting Business Intelligence from Online Product Reviews ijsc
The project proposes to build a system which is capable of extracting business intelligence for a manufacturer, from online product reviews. For a particular product, it extracts a list of the discussed features and their associated sentiment scores. Online products reviews and review characteristics are extracted from www.Amazon.com. A two level filtering approach is adapted to choose a set of reviews that are perceived to be useful by customers. The filtering process is based on the concept that the reviewer generated textual content and other characteristics of the review, influence peer customers in making purchasing choices. The filtered reviews are then processed to obtain a relative sentiment score associated with each feature of the product that has been discussed in these reviews. Based on these scores, the customer's impression of each feature of the product can be judged and used for the manufacturers benefit.
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Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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About
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.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
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.
Key Features
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.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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3. Introduction
Point 2
It becomes difficult to access what we are
looking for, so we need to organize ,understand
and summarize the information. Sentimental
analysis show us the compound sentiment of the
large set of reviews and topic modelling acts as
to tool to find a hidden topical pattern which is
present in the collection.
Point 4
This project contains dataset
of reviews and perform
various text pre-processing,
EDA, Sentimental analysis
and topic modelling to reach
to desired output.
Point 1
In recent years, the usage of E-
Commerce has increased the amount
of reviews given by the customer for a
particular product.
Point 3
Topic modelling can be described as a
method for finding a group of words
from a collection of data that best
represents the information in the
data.
4. Dataset Used
Dataset contains all the
reviews and respective
dates from various
category of smartphones
on Flipkart.
What is dataset all
about?
The whole dataset is
created using web scraping
from Flipkart using Python.
How the dataset is
created?
One lakh forty thousand
reviews
Number of reviews in
dataset
• Python
• Beautiful Soup, selenium
• requests
• Html
Tools and module used
for creating dataset
5. Methodology / model used
01
02
03
04
The project completely used Python language and its
various library for designing whole model.
Python
Using text pre-processing all the noise has been removed like
hashtags, emoji etc. Using EDA data has been analysed like
getting most frequent word in dataset, average word length etc.
EDA and text Pre-processing
Sentiment analysis is done to find the customer’s emotion. VADER library of
Python is used to perform Sentiment analysis. VADER is a lexicon and rule
based sentiment analysis tool.
Sentiment Analysis
LDA is used for topic modeling. It classify documents in different tags. We know
that LDA divides the given corpus in fixed number of topics and can also provide
which topics are contained in a document and with what probability.
LDA(Latent Dirichlet Allocation)
6. Result Achieved- 01
We have achieved either positive, negative or neutral sentiment using Vader sentiments and using
topic modelling we have categorize our model in seven different topics
Fig 1: Sample dataframe after computing sentiment analysis
Fig 2: graph of sentiment
analysis using Vader
9. Conclusion and Future Work
Conclusion-01
From the sentiment analysis that we have done
using VADER, we conclude that a larger portion
of the customer community favors or have
positive sentiment towards mobile phones
purchasing from Flipkart.
Conclusion-02
Using topic modelling we categorize
our dataset into seven different
topics according to their similarities
using LDA model.
Future work-01
we will consider using different
deep learning models and try
different and more complex
models in order to achieve better
results.
Future work-02
Additionally, we will verify the model over
larger datasets other than the given
dataset for better results.
01
02
03
04
10. References
• D. Blei, A. Ng, M. Jordan. Latent Dirichlet Allocation. Journal of
Machine Learning Research, 3: 993-1022, 2003.
• Jockers, Matthew & Thalken, Rosamond. (2020). Topic
modelling. 10.1007/978-3-030-39643-5_17.
• Hanna M. Wallach. 2006. Topic modeling: beyond bag-of-
words. In Proceedings of the 23rd international conference on
Machine learning (ICML ’06).