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Sentiment Analysis Classification Tasks Daniel Dakota R&D Seminar HLT Program September 1st, 2020 (R&D Seminar HLT Program) Sentiment Analysis Classification Tasks September 1st, 2020 1 / 15 Overview of Seminar Provide overview of


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Sentiment Analysis Classification Tasks

Daniel Dakota

R&D Seminar HLT Program

September 1st, 2020

(R&D Seminar HLT Program) Sentiment Analysis Classification Tasks September 1st, 2020 1 / 15

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Overview of Seminar

Provide overview of sentiment classification tasks Understand difficulty in annotating sentiment Be exposed to different sentiment strategies Able to select suitable approach to achieve specific sentiment goal

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Sentiment Analysis

What is Sentiment Analysis? Using computational methods to extract and quantify subjective

  • pinions

Applied to user reviews, customer feedback, survey responses among

  • thers

Desired in industry to help evaluate products Fun source of research into semantic usage of language

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Example Sentence

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!).

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Example Sentence

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!).

Is this a positive or negative review?

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Example Sentence

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!).

Is this a positive or negative review? How do we tell?

(R&D Seminar HLT Program) Sentiment Analysis Classification Tasks September 1st, 2020 4 / 15

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Example Sentence

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!).

Is this a positive or negative review? How do we tell? What features indicate this?

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Example Sentence

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!).

Is this a positive or negative review? How do we tell? What features indicate this? What are some difficulties?

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Sentiment Analysis

How do we develop models for sentiment? Supervised learning task Annotate data for various types of sentiment Feature engineering Semantic understanding

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Sentiment Areas Covered

Document level Sentence level Aspect based Multilingual Financial Abusive language

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Problems

Annotation schemes Human bias Domain specific properties Linguistic Issues (e.g. negation) Class imbalance Papers: Twitter as a Corpus for Sentiment Analysis and Opinion Mining Detection of Abusive Language: the Problem of Biased Datasets

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Document and Sentence Level

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!). Document vs Sentence Classification Types

Polarity (e.g. positive, neutral, negative) Classification (e.g. 1 star, buy, would recommend)

Papers: Thumbs up? Sentiment Classification using Machine Learning Techniques Determining the sentiment of opinions

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Aspect Based

The new iPhone has good battery life but the camera quality is not great. Would buy if reduced price with new contract, otherwise a bit too expensive and would stick with older iPhone (or better yet, just buy an Android!).

Which specific product is it? What is the sentiment of battery? What is the sentiment of camera? Papers: NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features

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Multilingual

How transferable are sentiment techniques across languages? What variation exists in how sentiment is expressed? Papers: Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks

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Financial

To what extent can news be used to predict financial markets? How effective are different domains? Within what time frames is information pertinent? Papers: Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines

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Abusive Language

What biases exist in the use of languages? How is abusive language expressed? How is abusive language perceived? How influential are topics on abusive language identification? Papers: Reducing Gender Bias in Abusive Language Detection

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Resources and Outlook

Numerous shared tasks provide extensive resources Large interest in social media reviews Extensive use of many mediums of communication (structured to unstructured) Growing interesting in other languages other ttan English Active area of research in both industry and academia resulting in numerous approaches on same data sets

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Project Suggestions

Detecting abusive language in Twitter Predicting stock market movements based on news headlines Predicting ratings of recipes based on user reviews

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Questions

Questions?

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