This document presents research on developing an automatic system to identify opinion leaders in group discussions. The system uses speech signal features like emotion and conversation ratios to score individuals. It was evaluated on both single and multiple datasets with 76% accuracy on emotion recognition. A field experiment applying the system to real group discussions achieved 73% accuracy in identifying the highest ranked opinion leader based on a combination of factors. The system provides a novel, simple and effective approach to opinion leader identification.
This document provides an overview of experimental design and sampling techniques in statistics. It defines key terms like population, sample, census, bias, and experimental units. It describes different sampling methods like simple random sampling, stratified sampling, cluster sampling, and multistage sampling. It also covers principles of experimental design like control, replication, and randomization. Specific experimental designs discussed include completely randomized design, block design, and matched pairs design. The document cautions about potential issues like nonresponse bias, response bias, and lack of realism in experiments.
This document provides an overview of experimental design and sampling techniques in statistics. It defines key terms like population, sample, census, bias, and experimental units. It discusses different sampling methods like simple random sampling, stratified random sampling, cluster sampling, and multistage sampling. It also covers principles of experimental design like control, replication, and randomization. Finally, it describes different types of experimental designs including completely randomized design, block design, and matched pairs design.
This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Mgs. Orlando Lizaldes E.
Ciclo: Sexto
Bimestre: Segundo
The document discusses different measures of central tendency (mean, median, mode) and how to determine which is most appropriate based on the type of data. It also covers measures of dispersion like range, standard deviation, and variance which provide information about how spread out values are from the central point. The mean is the most commonly used measure of central tendency but the median is less affected by outliers, while the mode represents the most frequent value.
The influence of social status on consensus building in collaboration networksIlire Hasani-Mavriqi
In this paper, we analyze the influence of social status on opinion dynamics and consensus building in collaboration networks. To that end, we simulate the diffusion of opinions in empirical collaboration networks by taking into account both the network structure and the individual differences of people reflected through their social status. For our simulations, we adapt a well-known Naming Game model and extend it with the Probabilistic Meeting Rule to account for the social status of individuals participating in a meeting. This mechanism is sufficiently flexible and allows us to model various situations in collaboration networks, such as the emergence or disappearance of social classes. In this work, we concentrate on studying three well-known forms of class society: egalitarian, ranked and stratified. In particular, we are interested in the way these society forms facilitate opinion diffusion. Our experimental findings reveal that (i) opinion dynamics in collaboration networks is indeed affected by the individuals’ social status and (ii) this effect is intricate and non-obvious. In particular, although the social status favors consensus building, relying on it too strongly can slow down the opinion diffusion, indicating that there is a specific setting for each collaboration network in which social status optimally benefits the consensus building process.
Paper: http://www.know-center.tugraz.at/cms/wp-content/uploads/2015/08/ASONAM_2015_Paper.pdf
Reference:
Hasani-Mavriqi I, Geigl F, Pujari SC, Lex E, Helic D (2015) The influence of social status on consensus building in collaboration networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, ASONAM ’15ACM, New York, NY, USA, pp 162–169
http://dl.acm.org/citation.cfm?id=2808887&CFID=851242713&CFTOKEN=32991930
This document provides an overview of experimental design and sampling techniques in statistics. It defines key terms like population, sample, census, bias, and experimental units. It describes different sampling methods like simple random sampling, stratified sampling, cluster sampling, and multistage sampling. It also covers principles of experimental design like control, replication, and randomization. Specific experimental designs discussed include completely randomized design, block design, and matched pairs design. The document cautions about potential issues like nonresponse bias, response bias, and lack of realism in experiments.
This document provides an overview of experimental design and sampling techniques in statistics. It defines key terms like population, sample, census, bias, and experimental units. It discusses different sampling methods like simple random sampling, stratified random sampling, cluster sampling, and multistage sampling. It also covers principles of experimental design like control, replication, and randomization. Finally, it describes different types of experimental designs including completely randomized design, block design, and matched pairs design.
This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
Universidad Técnica Particular de Loja
Ciclo Académico Abril Agosto 2011
Carrera: Inglés
Docente: Mgs. Orlando Lizaldes E.
Ciclo: Sexto
Bimestre: Segundo
The document discusses different measures of central tendency (mean, median, mode) and how to determine which is most appropriate based on the type of data. It also covers measures of dispersion like range, standard deviation, and variance which provide information about how spread out values are from the central point. The mean is the most commonly used measure of central tendency but the median is less affected by outliers, while the mode represents the most frequent value.
The influence of social status on consensus building in collaboration networksIlire Hasani-Mavriqi
In this paper, we analyze the influence of social status on opinion dynamics and consensus building in collaboration networks. To that end, we simulate the diffusion of opinions in empirical collaboration networks by taking into account both the network structure and the individual differences of people reflected through their social status. For our simulations, we adapt a well-known Naming Game model and extend it with the Probabilistic Meeting Rule to account for the social status of individuals participating in a meeting. This mechanism is sufficiently flexible and allows us to model various situations in collaboration networks, such as the emergence or disappearance of social classes. In this work, we concentrate on studying three well-known forms of class society: egalitarian, ranked and stratified. In particular, we are interested in the way these society forms facilitate opinion diffusion. Our experimental findings reveal that (i) opinion dynamics in collaboration networks is indeed affected by the individuals’ social status and (ii) this effect is intricate and non-obvious. In particular, although the social status favors consensus building, relying on it too strongly can slow down the opinion diffusion, indicating that there is a specific setting for each collaboration network in which social status optimally benefits the consensus building process.
Paper: http://www.know-center.tugraz.at/cms/wp-content/uploads/2015/08/ASONAM_2015_Paper.pdf
Reference:
Hasani-Mavriqi I, Geigl F, Pujari SC, Lex E, Helic D (2015) The influence of social status on consensus building in collaboration networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, ASONAM ’15ACM, New York, NY, USA, pp 162–169
http://dl.acm.org/citation.cfm?id=2808887&CFID=851242713&CFTOKEN=32991930
1. Experimental design refers to how experiments are structured in order to ensure validity and reliability of results.
2. There are several types of experimental designs including true experimental, quasi-experimental, pre-experimental, ex post facto, and factorial designs.
3. True experimental designs use random assignment and control/experimental groups to establish causation. Quasi-experimental designs lack random assignment so can only suggest relationships between variables. Ex post facto designs study pre-existing groups and cannot prove causation. Factorial designs study effects of multiple independent variables.
This document summarizes a student project on sentiment analysis of online movie reviews. The student used movie review data from Kaggle and performed text preprocessing techniques like stemming and lemmatization. Bag of Words and TF-IDF models were used to represent the text data. Naive Bayes and Random Forest classifiers were applied and evaluated. TF-IDF with Naive Bayes achieved the best accuracy of 84.71%. The project involved common NLP tasks like data collection, preprocessing, modeling and evaluation.
This document discusses various techniques for sentiment analysis and opinion mining at the document, sentence, and cross-language levels. It defines sentiment analysis as classifying opinion documents as positive or negative. At the document level, supervised learning techniques like naive Bayes and SVMs are commonly used. At the sentence level, subjectivity classification determines if a sentence is subjective or objective, while sentiment classification determines if a subjective sentence is positive, negative, or neutral. Cross-language techniques include translation and lexicon-based approaches.
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This document discusses design and its meaning, as well as two experiments related to logo design. It defines design as the human capacity to shape our environment in novel ways that meet our needs. The first experiment tests how well five logos for a business school match that school's mission statements of being dynamic, credible, and international. The second experiment examines logo designs for an intercontinental bus company along the Silk Road, testing how well designs communicate being on time, safe, and affordable. Both experiments find designs strongest at communicating safety and responsibility, with more individual variation in communicating timeliness. In general, designs can transfer meaning pragmatically, and cultural differences and variation in perceptions need to be considered in design.
Point estimate for a population proportion pMuel Clamor
This document provides information about point estimates for population proportions:
1) A point estimate predicts a parameter with a single number, while an interval estimate provides a range of numbers that could be the true parameter value.
2) The point estimator for a population proportion p is the sample proportion p, which is calculated as the number of successes divided by the sample size n.
3) Two examples are given to demonstrate calculating the point estimate of a population proportion p from sample data on the number of successes.
Session 2 into to qualitative research introAngela Ferrara
This document provides guidance on conducting qualitative research. It discusses key aspects of the research process such as developing a conceptual framework, determining what and who to study, collecting data through methods like interviews and observation, and analyzing the data through techniques such as coding and creating displays. The document emphasizes generating conclusions that consider alternative explanations and testing findings for reliability and generalizability.
SEMATIC DIFFERENTIAL SCALE AND SUMMATED SCALE.pptxSiyonaBansode
This document provides an overview of semantic differential scales, summated scales, and sociometry. It defines each technique and discusses their characteristics, advantages, disadvantages, limitations, and examples of how they are used. Specifically, the semantic differential scale is described as a method to measure psychological meanings through bipolar adjective ratings. Steps in its use and examples are outlined. Summated scales are defined as assessment tools used at the end of a course to evaluate overall achievement of objectives. Sociometry is introduced as a way to measure interpersonal relationships through techniques like sociograms and matrices.
The document discusses sample design and data collection for impact evaluations, covering topics like sampling techniques, sample size calculations, developing a data collection plan, training enumerators, and pilot testing instruments. It provides an example of using a stratified multi-stage sampling design with 7,000 households to evaluate the socioeconomic impact of a water program. The goal of impact evaluations is to allow for the estimation and hypothesis testing of program impacts through rigorous evaluation and sample designs.
The document outlines an agenda for a design sprint workshop to improve the airport experience for passengers flying out of Boston Logan Airport. The workshop will follow a design sprint methodology over 5 days to: 1) Understand passengers and their needs through empathy mapping and assumption analysis, 2) Generate ideas through jobs stories and brainstorming techniques, 3) Converge on ideas to test through sketching and feedback, 4) Prototype the top idea, and 5) Test the prototype with passengers and analyze the results to identify validations or invalidations. The goal is to apply human-centered design processes to identify an experience that improves passenger satisfaction from the start of their airport journey.
Southwest Airlines has hired the design team to improve the passenger experience at Boston Logan Airport from arrival to departure. On the first day, the team conducted assumption storming and empathy mapping to understand passenger pain points. They defined the problem as making passengers happy during their pre-flight experience. On day two, the team generated ideas through job stories and six-ups. On day three, they converged on ideas through sketching and $100 testing. Day four involved prototyping the selected idea. On the final day, the team tested their prototype with passengers and analyzed the results.
This document discusses sampling design and measurement of variables in research. It covers:
- The definition and reasons for sampling, including reducing costs, time and errors compared to a full census.
- Key considerations for sample size decisions including the population, elements, frame, sample, units and subject of study. Larger samples are needed for multivariate or experimental research.
- Common sampling techniques like simple random, systematic, cluster and stratified sampling as well as sources of sampling error.
- The importance of clearly defining variables through operationalization and use of appropriate scales like nominal, ordinal, interval and ratio scales for measurement.
The document provides information about an upcoming bootcamp on natural language processing (NLP) being conducted by Anuj Gupta. It discusses Anuj Gupta's background and experience in machine learning and NLP. The objective of the bootcamp is to provide a deep dive into state-of-the-art text representation techniques in NLP and help participants apply these techniques to solve their own NLP problems. The bootcamp will be very hands-on and cover topics like word vectors, sentence/paragraph vectors, and character vectors over two days through interactive Jupyter notebooks.
April 10th of 2018 budapest presentationAhmet Bulut
This document describes a method for transfer learning to perform sentiment classification when only a small labeled dataset exists for a language. The method uses pre-trained word embeddings from Facebook's fastText to represent words. A multi-class classifier is trained on labeled English sentiment data and fine-tuned on the smaller labeled target language data. Results show the transfer learning approach improves prediction accuracy over training only on the smaller target language data.
This document discusses multimodal learning analytics (MLA), which examines learning through multiple modalities like video, audio, digital pens, etc. It provides examples of extracting features from these modalities to analyze problem-solving sessions. Video features like total movement, distance from table, and calculator tracking are described. Audio features like speech duration and word counts are mentioned. Digital pen features like strokes, pressure, and shapes are examined. The document concludes that MLA has much potential to explore learning in more realistic settings compared to traditional learning analytics.
Attitude scale construction by sakshi shastrisakshishastri3
This document discusses two methods for constructing attitude scales: paired comparison and equal appearing interval technique. It provides definitions and examples of each technique. The paired comparison method involves respondents selecting between two objects according to some criterion, allowing for ranking of items from most to least preferred. The equal appearing interval method involves subject matter experts sorting statements into piles from most favorable to unfavorable to derive scale values. Advantages and disadvantages of each method are listed.
The document discusses various steps involved in analyzing and interpreting data, including developing an analysis plan, collecting and cleaning data, analyzing the data using appropriate techniques, interpreting the results by drawing conclusions and recommendations while also considering limitations. It provides examples of different analysis techniques like descriptive statistics, inferential statistics, and qualitative data analysis and emphasizes the importance of interpreting data in the context of the research questions.
Conversational transfer learning for emotion recognitionTakato Hayashi
1) The document proposes an approach called TL-ERC that uses transfer learning to improve emotion recognition in conversations. TL-ERC pre-trains a hierarchical dialogue model on multi-turn conversation data and transfers its parameters to an emotion classifier.
2) Experiments show that TL-ERC improves performance and robustness over randomly initialized models, especially with limited training data. TL-ERC also reaches optimal validation performance in fewer training epochs.
3) Comparisons indicate TL-ERC outperforms previous state-of-the-art models for emotion recognition and is better able to leverage pre-trained weights than training from scratch.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
This document summarizes an R boot camp focusing on statistics. It includes an agenda that covers introducing the lab component, R basics, descriptive statistics in R, revisiting installation instructions, and measures of variability in R. Descriptive statistics are presented as ways to characterize data through measures of central tendency, shape, and variability. Examples are provided in R for calculating the mean, median, mode, range, percentiles, variance, standard deviation, and coefficient of variation. The central limit theorem and standardizing scores are also discussed. Real-world applications of R for clean and messy data are mentioned.
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
1. Experimental design refers to how experiments are structured in order to ensure validity and reliability of results.
2. There are several types of experimental designs including true experimental, quasi-experimental, pre-experimental, ex post facto, and factorial designs.
3. True experimental designs use random assignment and control/experimental groups to establish causation. Quasi-experimental designs lack random assignment so can only suggest relationships between variables. Ex post facto designs study pre-existing groups and cannot prove causation. Factorial designs study effects of multiple independent variables.
This document summarizes a student project on sentiment analysis of online movie reviews. The student used movie review data from Kaggle and performed text preprocessing techniques like stemming and lemmatization. Bag of Words and TF-IDF models were used to represent the text data. Naive Bayes and Random Forest classifiers were applied and evaluated. TF-IDF with Naive Bayes achieved the best accuracy of 84.71%. The project involved common NLP tasks like data collection, preprocessing, modeling and evaluation.
This document discusses various techniques for sentiment analysis and opinion mining at the document, sentence, and cross-language levels. It defines sentiment analysis as classifying opinion documents as positive or negative. At the document level, supervised learning techniques like naive Bayes and SVMs are commonly used. At the sentence level, subjectivity classification determines if a sentence is subjective or objective, while sentiment classification determines if a subjective sentence is positive, negative, or neutral. Cross-language techniques include translation and lexicon-based approaches.
Emotions and Feeling Markets Go Grow Presentation feb.2017Kelvina Wairimu
This document discusses design and its meaning, as well as two experiments related to logo design. It defines design as the human capacity to shape our environment in novel ways that meet our needs. The first experiment tests how well five logos for a business school match that school's mission statements of being dynamic, credible, and international. The second experiment examines logo designs for an intercontinental bus company along the Silk Road, testing how well designs communicate being on time, safe, and affordable. Both experiments find designs strongest at communicating safety and responsibility, with more individual variation in communicating timeliness. In general, designs can transfer meaning pragmatically, and cultural differences and variation in perceptions need to be considered in design.
Point estimate for a population proportion pMuel Clamor
This document provides information about point estimates for population proportions:
1) A point estimate predicts a parameter with a single number, while an interval estimate provides a range of numbers that could be the true parameter value.
2) The point estimator for a population proportion p is the sample proportion p, which is calculated as the number of successes divided by the sample size n.
3) Two examples are given to demonstrate calculating the point estimate of a population proportion p from sample data on the number of successes.
Session 2 into to qualitative research introAngela Ferrara
This document provides guidance on conducting qualitative research. It discusses key aspects of the research process such as developing a conceptual framework, determining what and who to study, collecting data through methods like interviews and observation, and analyzing the data through techniques such as coding and creating displays. The document emphasizes generating conclusions that consider alternative explanations and testing findings for reliability and generalizability.
SEMATIC DIFFERENTIAL SCALE AND SUMMATED SCALE.pptxSiyonaBansode
This document provides an overview of semantic differential scales, summated scales, and sociometry. It defines each technique and discusses their characteristics, advantages, disadvantages, limitations, and examples of how they are used. Specifically, the semantic differential scale is described as a method to measure psychological meanings through bipolar adjective ratings. Steps in its use and examples are outlined. Summated scales are defined as assessment tools used at the end of a course to evaluate overall achievement of objectives. Sociometry is introduced as a way to measure interpersonal relationships through techniques like sociograms and matrices.
The document discusses sample design and data collection for impact evaluations, covering topics like sampling techniques, sample size calculations, developing a data collection plan, training enumerators, and pilot testing instruments. It provides an example of using a stratified multi-stage sampling design with 7,000 households to evaluate the socioeconomic impact of a water program. The goal of impact evaluations is to allow for the estimation and hypothesis testing of program impacts through rigorous evaluation and sample designs.
The document outlines an agenda for a design sprint workshop to improve the airport experience for passengers flying out of Boston Logan Airport. The workshop will follow a design sprint methodology over 5 days to: 1) Understand passengers and their needs through empathy mapping and assumption analysis, 2) Generate ideas through jobs stories and brainstorming techniques, 3) Converge on ideas to test through sketching and feedback, 4) Prototype the top idea, and 5) Test the prototype with passengers and analyze the results to identify validations or invalidations. The goal is to apply human-centered design processes to identify an experience that improves passenger satisfaction from the start of their airport journey.
Southwest Airlines has hired the design team to improve the passenger experience at Boston Logan Airport from arrival to departure. On the first day, the team conducted assumption storming and empathy mapping to understand passenger pain points. They defined the problem as making passengers happy during their pre-flight experience. On day two, the team generated ideas through job stories and six-ups. On day three, they converged on ideas through sketching and $100 testing. Day four involved prototyping the selected idea. On the final day, the team tested their prototype with passengers and analyzed the results.
This document discusses sampling design and measurement of variables in research. It covers:
- The definition and reasons for sampling, including reducing costs, time and errors compared to a full census.
- Key considerations for sample size decisions including the population, elements, frame, sample, units and subject of study. Larger samples are needed for multivariate or experimental research.
- Common sampling techniques like simple random, systematic, cluster and stratified sampling as well as sources of sampling error.
- The importance of clearly defining variables through operationalization and use of appropriate scales like nominal, ordinal, interval and ratio scales for measurement.
The document provides information about an upcoming bootcamp on natural language processing (NLP) being conducted by Anuj Gupta. It discusses Anuj Gupta's background and experience in machine learning and NLP. The objective of the bootcamp is to provide a deep dive into state-of-the-art text representation techniques in NLP and help participants apply these techniques to solve their own NLP problems. The bootcamp will be very hands-on and cover topics like word vectors, sentence/paragraph vectors, and character vectors over two days through interactive Jupyter notebooks.
April 10th of 2018 budapest presentationAhmet Bulut
This document describes a method for transfer learning to perform sentiment classification when only a small labeled dataset exists for a language. The method uses pre-trained word embeddings from Facebook's fastText to represent words. A multi-class classifier is trained on labeled English sentiment data and fine-tuned on the smaller labeled target language data. Results show the transfer learning approach improves prediction accuracy over training only on the smaller target language data.
This document discusses multimodal learning analytics (MLA), which examines learning through multiple modalities like video, audio, digital pens, etc. It provides examples of extracting features from these modalities to analyze problem-solving sessions. Video features like total movement, distance from table, and calculator tracking are described. Audio features like speech duration and word counts are mentioned. Digital pen features like strokes, pressure, and shapes are examined. The document concludes that MLA has much potential to explore learning in more realistic settings compared to traditional learning analytics.
Attitude scale construction by sakshi shastrisakshishastri3
This document discusses two methods for constructing attitude scales: paired comparison and equal appearing interval technique. It provides definitions and examples of each technique. The paired comparison method involves respondents selecting between two objects according to some criterion, allowing for ranking of items from most to least preferred. The equal appearing interval method involves subject matter experts sorting statements into piles from most favorable to unfavorable to derive scale values. Advantages and disadvantages of each method are listed.
The document discusses various steps involved in analyzing and interpreting data, including developing an analysis plan, collecting and cleaning data, analyzing the data using appropriate techniques, interpreting the results by drawing conclusions and recommendations while also considering limitations. It provides examples of different analysis techniques like descriptive statistics, inferential statistics, and qualitative data analysis and emphasizes the importance of interpreting data in the context of the research questions.
Conversational transfer learning for emotion recognitionTakato Hayashi
1) The document proposes an approach called TL-ERC that uses transfer learning to improve emotion recognition in conversations. TL-ERC pre-trains a hierarchical dialogue model on multi-turn conversation data and transfers its parameters to an emotion classifier.
2) Experiments show that TL-ERC improves performance and robustness over randomly initialized models, especially with limited training data. TL-ERC also reaches optimal validation performance in fewer training epochs.
3) Comparisons indicate TL-ERC outperforms previous state-of-the-art models for emotion recognition and is better able to leverage pre-trained weights than training from scratch.
NLP Bootcamp 2018 : Representation Learning of text for NLPAnuj Gupta
The document provides an outline for a workshop on representation learning of text for natural language processing (NLP). The workshop will be divided into 4 modules covering both foundational techniques like one-hot encoding and bag-of-words as well as state-of-the-art methods like word, sentence, and character vectors. The objective is for participants to gain a deeper understanding of the key ideas, math, and code behind text representation techniques in order to apply them to solve NLP problems and achieve higher accuracies and understanding.
This document summarizes an R boot camp focusing on statistics. It includes an agenda that covers introducing the lab component, R basics, descriptive statistics in R, revisiting installation instructions, and measures of variability in R. Descriptive statistics are presented as ways to characterize data through measures of central tendency, shape, and variability. Examples are provided in R for calculating the mean, median, mode, range, percentiles, variance, standard deviation, and coefficient of variation. The central limit theorem and standardizing scores are also discussed. Real-world applications of R for clean and messy data are mentioned.
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
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3. INTRODUCTION
• In the era of information explosion, most of the
information seem to be neglected due to the lack of
efficient influence on the receivers.
• There are many ways to promote a product or service.
• Word-of-mouth (WOM) dissemination has stronger
influence on consumer decisions than advertising. [1]
• People often seek for comments.
AbstractINTRODUCTION 2 / 22
4. INTRODUCTION (cont.)
• In word-of-mouth communication, the person who has
the highest influence among others is called the
opinion leader. [2]
• An opinion leader, with his/her ideas and power of oral
communication skills, often becomes an important
reference for a person while making a decision.
• To identify an opinion leader becomes an essential
topic since the opinion leader offers the highest effect
and efficiency of product or service promotion on
marketing. [3-5]
Opinion LeaderINTRODUCTION 3 / 22
5. INTRODUCTION - Contributions
• The contribution of this paper is three-fold:
1. We have proposed a novel approach to conduct
the opinion leader identification by using features
of speech signals.
2. We have proposed 3 algorithms for opinion leader
identification.
3. We have proposed automatic opinion leader
recognizing system, which is composed of a simple
and efficient model to do the opinion leader
identification.
Contributions of this WorkINTRODUCTION 4 / 22
6. RELATED WORK
• “Algorithm of identifying opinion leaders in BBS” [6]
- Analyze the text to do the identification
- PageRank Algorithm
• “Understanding opinion leaders in bulletin board systems:
Structures and algorithms” [7]
- First, analyze the characteristic of opinion leader
- Then, analyze the emotion through the emotion mining methods
• “Research on Methods to Identify the Opinion Leaders in
Internet Community” [8]
- Analyze the influence and the emotion express of the content
- PageRank Algorithm
Other StudiesRELATED WORK 5 / 22
7. SYSTEM DESIGN - Flowchart
• The proposed system flowchart:
The System FlowchartSYSTEM DESIGN 6 / 22
Speech Data Input
Emotion Ratio (E)
Measurement
Conversation Ratio (C)
Measurement
Score of Influential Capacity (S)
Measurement
Opinion Leader
Identification
8. SYSTEM DESIGN – Conversation Ratio
• Conversation Ratio (C) Measurement
- The time spoken by each person in the discussion
Conversation Ratio (C)SYSTEM DESIGN 7 / 22
Parameters Description
L the total discussion time
Ni
the number of times spoken by the i-th person
during total discussion time
λi the speaking frequency of the i-th person
ti,j
the time spent by the i-th person in his j-th
speaking
Ti
the average of each speaking time spent by the i-
th person
Ci the Conversation Ratio of the i-th person
9. SYSTEM DESIGN – Conversation Ratio (cont.)
• Conversation Ratio (C) Measurement
- The Equations:
𝜆" =
𝑁"
𝐿
𝑇" =
∑ 𝑡",*
+,
*-.
𝑁"
𝐶" = 𝜆" ∗ 𝑇"
where 𝐶" ranges from 0 to 1
Conversation Ratio (C)SYSTEM DESIGN 8 / 22
10. SYSTEM DESIGN – Emotion Ratio
• Emotion Ratio (E) Measurement
- The emotion of each member during their speaking in group
discussion
- The Equation:
𝐸" =
∑ 𝑒",*
3,
*-.
𝑀"
Emotion Ratio (E)SYSTEM DESIGN 9 / 22
Parameters Description
Mi
the total number of sentences the i-th person
speaks
ei,j
the result of the emotion recognition on the j-th
sentence spoken by the i-th person
Ei the Emotion Ratio of the i-th person
11. SYSTEM DESIGN – Score of Influential Capacity
• Score of Influential Capacity (S) Measurement
- The Equation:
𝑆" = 𝐶" ∗ 𝐸" + 1
- The value of C and E are positively proportional; therefore,
we adopt multiplication to compute the score (S).
- To avoid an extreme case, E is increased by 1.
Score of Influential Capacity (S)SYSTEM DESIGN 10 / 22
12. EVALUATION - Overview
• Model Training:
We adopted a single dataset using Berlin Database of
Emotional Speech to build the model.
• Model Testing:
We cut the selected YouTube videos into sentences and used
these data as multiple datasets to test our model.
Single
Dataset
Feature
Extraction
Support Vector
Machine (SVM)
Multiple
Datasets
Result
Feature
Extraction
Support Vector
Machine (SVM)
Trained Model
Training
Emotion
Classification
OverviewEVALUATION 11 / 22
13. EVALUATION – Emotion Recognition
• Emotions Classification:
• Feature Extraction: [9]
- We adopted the openSMILE library to do the feature
extraction.
- We chose Energy, Pitch, and Mel-scale Frequency Cepstral
Coefficients (MFCC) of the speech signal to do the analysis.
- We used the support vector machine (SVM) to classify the
speech data.
Emotion RecognitionEVALUATION 12 / 22
Neutral Speech Emotional Speech
The speech w/o any emotion
The speech w/ emotion,
No matter what kind of emotion
the speech carries
emotion recognition result (e) = 0 emotion recognition result (e) = 1
14. EVALUATION – Single Dataset
• Single Dataset Testing Result
– Dataset: Berlin Database of Emotional Speech
*N: Neutral, E: Emotional
Since both Model 1 and Model 3 got the same highest overall accuracy,
we further test them by the multiple datasets.
Single DatasetEVALUATION 13 / 22
Model 1 Model 2 Model 3 Model 4
Features MFCC MFCC + Energy MFCC + Pitch MFCC + Energy + Pitch
Labels* N E N E N E N E
Recall 78 % 98 % 75 % 98 % 76 % 98 % 70 % 98 %
Precision 88 % 95 % 90 % 94 % 90 % 94 % 89 % 93 %
F-measure 82 % 96 % 81 % 95 % 82 % 95 % 78 % 95 %
Overall
Accuracy
94.68 % 94.41 % 94.68 % 93.61 %
15. EVALUATION – Multiple Datasets
• Multiple Datasets Test
̶ We chose 10 YouTube Videos to be our testing data.
̶ The language spoken in these videos includes German, Chinese,
and English
̶ The content includes the fragments of a comedy, quarrel, or
sadness.
̶ Normalization:
𝑉9
=
𝑉3:;
9
∗ 𝑉 − 𝑉=">
𝑉3:; − 𝑉=">
, where 𝑉3:;
9
denotes the maximum number designed by us
to fit the range of SVM input value.
The original feature value ranges from 𝑉="> to 𝑉3:;.
After the normalization, the range would be 𝑉=">
9
to 𝑉3:;
9
.
Multiple DatasetsEVALUATION 14 / 22
16. EVALUATION – Multiple Datasets (cont.)
• Multiple Datasets Testing Result
̶ Since Model 3 got the better result, we use Model 3 to do
the field experiment.
Multiple DatasetsEVALUATION 15 / 22
Model 1 Model 3
Neutral 41 % 55 %
Emotional 76 % 89 %
Accuracy 62 % 76 %
17. EVALUATION – Field Experiment
• The field experiment contains 10 groups with 5
members in each group.
• These 10 groups are constituted with 8 men and 8
women.
• 10 different topics and different discussion time are set
in each group.
• 3 observers are assigned to observe the 5 members
while discussing.
• The observer would give the ranking, from Rank 1 to
Rank 5.
Field ExperimentEVALUATION 16 / 22
18. EVALUATION – Field Experiment (cont.)
• Consistency among rankings by 3 observers in the field
experiment
Observer ConsistencyEVALUATION 17 / 22
91%
19. EVALUATION – Field Experiment (cont.)
• Experimental Result (Heat Map)
Comparison (C, E)EVALUATION 18 / 22
Comparison between the rankings
using Conversation Ratio (C)
Comparison between the rankings using
Emotion Ratio (E)
𝑥 𝑥
𝑦 𝑦
18
19
16
14
20
11
7
11
10
9
18 11
20. EVALUATION – Field Experiment (cont.)
• Experimental Result
Comparison (S)EVALUATION 19 / 22
Comparison between the rankings using
the Score of Influential Capacity (S)
22
21. EVALUATION – Field Experiment (cont.)
• Experimental Result
Cumulative Distribution FunctionEVALUATION 20 / 22
CDF of conversation ratio (C) and emotion ratio (E)
in the field experiment
> 90%
35%
35%
~ 100%
50%
Comparison using Emotion Ratio (E) only
22. EVALUATION – Field Experiment (cont.)
• The results of accuracy measurement:
• Combining the 2 factors will have the better overall
result, which is 73 % accuracy on Rank 1.
Experimental ResultEVALUATION 21 / 22
Rank 1 Rank 2 Rank 3 Rank 4 Rank 5
𝐶" 60 % 58 % 53 % 52 % 67 %
𝐸" 33 % 26 % 26 % 28 % 75 %
𝑆" 73 % 55 % 47 % 63 % 73 %
22
16
14
19
22
22 / 30 16 / 30 14 / 30 19 / 30 22 / 30
Comparison using
the Score of Influential
Capacity (S)
23. CONCLUSION
• In this paper, a novel approach is proposed to identify
the opinion leader in a group discussion by evaluating
the degree of participation and the emotion expression
of the speech for each person.
• Through the field experiment, we proved our approach
have a 73 % accuracy to achieve our goal.
• Our method is simple, efficient and effective.
ConclusionCONCLUSION 22 / 22
24. REFERENCES
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