Sentiment Analysis Basics
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Questions and Answers

What term is synonymous with sentiment analysis?

  • Data Mining
  • Emotion Recognition
  • Language Processing
  • Opinion Mining (correct)

In the context of sentiment analysis, what does the term 'opinion holder' refer to?

  • The subject being judged
  • The method used to analyze sentiments
  • The individual expressing the opinion (correct)
  • The context in which the opinion is given

Which of the following accurately describes 'opinion content'?

  • The relevance of the opinion to the opinion target
  • The specific statement expressing the opinion (correct)
  • The overall sentiment classification of the opinion
  • The historical context of the opinion given

What does 'opinion sentiment' indicate about an opinion?

<p>Whether the opinion is favorable or unfavorable (D)</p> Signup and view all the answers

In the example, 'Peter (iPhone 15): It is too expensive', what is the opinion target?

<p>iPhone 15 (D)</p> Signup and view all the answers

What aspect of an opinion provides context regarding the conditions of its expression?

<p>Opinion context (D)</p> Signup and view all the answers

Which of the following components specifically identifies what an opinion is about?

<p>Opinion content (B)</p> Signup and view all the answers

In sentiment analysis, what characteristic does 'opinion polarity' describe?

<p>The emotional tone of the opinion (A)</p> Signup and view all the answers

Which of the following tasks is NOT commonly associated with sentiment analysis?

<p>Predictive Sentiment Modeling (B)</p> Signup and view all the answers

What is a primary application of sentiment analysis in the healthcare domain?

<p>Evaluating patient treatment experiences on social media (D)</p> Signup and view all the answers

Which data preprocessing step involves removing insignificant words from a dataset?

<p>Stop words removal (C)</p> Signup and view all the answers

What is a significant limitation of the lexicon-based sentiment analysis approach?

<p>It is dependent on word meanings and context variability (C)</p> Signup and view all the answers

Which of the following is considered a resource for data collection in sentiment analysis?

<p>Crowdsourcing platforms (D)</p> Signup and view all the answers

In the context of sentiment analysis, term frequency-inverse document frequency (TF-IDF) is primarily used for:

<p>Measuring word relevance across documents (B)</p> Signup and view all the answers

Which technique is NOT part of general sentiment analysis procedures?

<p>Data aggregation (A)</p> Signup and view all the answers

Which of the following platforms is primarily utilized for sentiment analysis demonstrations?

<p>Dandelion (C)</p> Signup and view all the answers

What does Aspect-based Sentiment Analysis (ABSA) primarily consider?

<p>Both the sentiment and the target information (D)</p> Signup and view all the answers

Which of the following tasks is NOT part of the SemEval-2014 Task 4?

<p>Sentiment classification on a document level (B)</p> Signup and view all the answers

What kind of model is SentiBERT categorized as?

<p>A language model-based approach (D)</p> Signup and view all the answers

Which early work applied an Adaptive Recursive Neural Network for ABSA?

<p>Dong et al. (2014) (D)</p> Signup and view all the answers

What key element do target-dependent LSTMs introduced by Tang et al. (2016) avoid using?

<p>Internal parse trees (C)</p> Signup and view all the answers

What main action does the first step of neural approaches in ABSA involve?

<p>Representing context of a target (B)</p> Signup and view all the answers

What technology did Wang et al. (2016b) notably utilize in their proposal for ABSA?

<p>Attention-based LSTM (A)</p> Signup and view all the answers

In the SST-5 classification task, how many classes are used?

<p>5 classes (A)</p> Signup and view all the answers

What unique mechanism did Zheng et al. (2019) propose for Aspect-based Sentiment Analysis (ABSA)?

<p>Local Context Focus mechanism (D)</p> Signup and view all the answers

What does a true positive in Targeted F1 metric require?

<p>Exact extraction of sentiment target and correct polarity (C)</p> Signup and view all the answers

How did Rietzler et al. (2020) modify the BERT input format for ABSA?

<p>[CLS] sent [SEP] aspects [SEP] (B)</p> Signup and view all the answers

Which task was NOT part of SemEval 2022 Task 10 on Structured Sentiment Analysis?

<p>Sentiment polarity analysis (D)</p> Signup and view all the answers

What primary benefit does the Sentiment Graph offer in Structured Sentiment Analysis?

<p>Comprehensive representation of sentiment information (B)</p> Signup and view all the answers

Which of the following datasets was NOT mentioned as being used in SemEval 2022 Task 10?

<p>Sentiment140 (A)</p> Signup and view all the answers

What aspect of BERT did Zheng et al. (2019) utilize for Aspect-based Sentiment Analysis?

<p>Local Context Focus mechanism (C)</p> Signup and view all the answers

What differentiates the SSA introduced by Barnes et al. (2021) from traditional sentiment analysis?

<p>Use of Sentiment Graphs to depict relationships (B)</p> Signup and view all the answers

What is the main focus of the research presented by B. Shin and colleagues in 2016?

<p>Integration of lexicons within CNN models for sentiment analysis (D)</p> Signup and view all the answers

Which aspect of sentiment analysis does the paper by Li et al. (2016) primarily address?

<p>Group-level sentiment analysis for movie recommendations (D)</p> Signup and view all the answers

In which publication was the survey on deep learning for sentiment analysis by Zhang et al. (2018) published?

<p>Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (A)</p> Signup and view all the answers

What methodology is highlighted in the study by Dozat and Manning (2018)?

<p>Semantic dependency parsing with improved accuracy (A)</p> Signup and view all the answers

What was the primary topic of research conducted by Georgiadou et al. (2020)?

<p>Sentiment analysis of Brexit negotiation outcomes (B)</p> Signup and view all the answers

Which research topic is explored by Kraaijeveld and De Smedt (2020)?

<p>Financial market predictions based on sentiment analysis (A)</p> Signup and view all the answers

According to the research by Rietzler et al. (2020), which technique is emphasized for sentiment classification?

<p>Domain adaptation via fine-tuning of BERT models (C)</p> Signup and view all the answers

What is the main innovation presented in the work of Zeng et al. (2019)?

<p>A local context focus mechanism for sentiment classification (C)</p> Signup and view all the answers

Which method is emphasized for target-dependent Twitter sentiment classification?

<p>Adaptive recursive neural networks (C)</p> Signup and view all the answers

Identify the study that proposed combining convolutional and recurrent neural networks for sentiment analysis.

<p>Wang et al. (2016) study (A)</p> Signup and view all the answers

Which research specifically focuses on lexicon-based methods for sentiment analysis?

<p>Taboada et al. (2011) (B)</p> Signup and view all the answers

What technique is NOT mentioned as being used for sentiment classification in the document?

<p>Logistic regression (D)</p> Signup and view all the answers

Who authored the paper that presents effective LSTMs for target-dependent sentiment classification?

<p>Duyu Tang et al. (C)</p> Signup and view all the answers

Which conference proceedings include a paper on predicting polarities of tweets?

<p>ACL 2015 (D)</p> Signup and view all the answers

What was the main contribution of the paper by Hong et al. (2013)?

<p>Exploring the issues of multilingual sentiment analysis (D)</p> Signup and view all the answers

Which study focuses on using attention mechanisms within LSTM for sentiment classification?

<p>Wang, Y. et al. (2016b) (C)</p> Signup and view all the answers

Flashcards

Sentence-level Sentiment Classification

The task of classifying text into categories like positive, negative, or neutral based on the emotional tone expressed.

Aspect-based Sentiment Analysis (ABSA)

Identifying specific aspects of a product or service, along with the sentiment associated with each aspect.

Structured Sentiment Analysis (SSA)

Extracting structured information, like sentiment labels or emotions, from text.

Data collection and extraction

The process of gathering and preparing text data for sentiment analysis.

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Data preprocessing

Preparing collected text data for analysis by performing tasks like tokenization, stop word removal, and lemmatization.

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Feature extraction

Identifying important features within the text, such as words, phrases, or parts of speech, that reflect the overall sentiment.

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Lexicon-based approach

A sentiment analysis approach that uses a predefined list of words with assigned sentiment scores to determine the overall sentiment of a text.

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Domain dependency

The challenge in lexicon-based approaches where words can have different meanings depending on the context.

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Opinion Holder

Identifying the person, entity, or group expressing the sentiment or opinion, often mentioned explicitly or implicitly in the text.

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Opinion Target

The specific topic or object that is being discussed or evaluated in the opinion. This can be a product, service, person, place, or event.

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Opinion Content

The words or phrases used to express the opinion directly. It reveals the author's attitude or sentiment.

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Opinion Context

The context in which the opinion is expressed, including factors like time, location, situation, or other related information.

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Opinion Sentiment (or Polarity)

Categorizing an opinion as positive, negative, or neutral, reflecting the emotional tone or feeling expressed towards the opinion target.

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Opinion

A text that openly expresses a perspective or evaluation about a specific topic, often found in the context of product reviews, social media posts, or news articles.

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Sentiment Analysis

The process of extracting, analyzing, and understanding opinions and sentiments expressed in text data.

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Opinion Mining

The field of study that focuses on analyzing and understanding user-generated content, particularly opinions and sentiments, to gain valuable insights from it.

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Lexicon-based Sentiment Analysis

A sentiment analysis method that uses a predefined list of words with sentiment scores to calculate the overall sentiment of text.

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Machine Learning for Sentiment Analysis

A system that uses machine learning to learn patterns from data and classify text based on sentiment.

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Neural Network Sentiment Analysis

A technique that uses neural networks to analyze text and determine sentiment.

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Aspect-Based Sentiment Analysis

A sentiment analysis approach that focuses on identifying specific aspects of an entity and the sentiment associated with each aspect.

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Deep Learning for Sentiment Analysis

A sentiment analysis technique that uses deep learning to capture longer-range dependencies and context in text.

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Target-Dependent Sentiment Analysis

Analyzing text related to a specific topic, for example, customer reviews for a product.

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Sentence-level Sentiment Analysis

Analyzing sentiment at the level of a single sentence.

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Local Context Focus (LCF)

A technique in Aspect-Based Sentiment Analysis (ABSA) that focuses on the relevant context surrounding a specific aspect to determine its sentiment.

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Fine-tuning BERT for ABSA

A method in ABSA that uses BERT, a powerful language model, by fine-tuning it specifically for ABSA tasks.

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Targeted F1 Score

A metric used in ABSA to assess the accuracy of sentiment prediction, taking into account both the correct identification of the sentiment target and its polarity.

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Sentiment Graph

An approach for structured sentiment analysis, using a structured representation like a sentiment graph, which focuses on extracting and organizing all related opinion and sentiment information from text.

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MultiBooked

A collection of reviews regarding hotels in Basque and Catalan languages, gathered from booking.com, used for evaluating structured sentiment analysis models.

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Stanford Sentiment Treebank (SST)

A publicly available dataset for sentiment analysis, containing text annotated with sentiment categories at both the phrase and sentence levels.

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Aspect Term Extraction

A task in ABSA involving identifying the specific elements or aspects being discussed in a piece of text.

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Aspect Term Polarity (ATSC)

A task in ABSA that aims to determine the sentiment expressed towards an identified aspect term.

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Aspect Category Detection

A task in ABSA that identifies the category or type of aspect being discussed, such as 'food', 'service', or 'ambience'.

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Aspect Category Polarity

A task in ABSA that determines the sentiment expressed towards a specific aspect category.

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SentiBERT

A language model specifically trained for sentiment analysis, enabling it to understand the emotional tones and nuances of text.

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Study Notes

Text Mining: Sentiment Analysis

  • Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone behind text.
  • An opinion is a subjective statement about something, reflecting a person's belief or thought.
  • It's different from factual statements which can be proven right or wrong.

Introduction

  • Opinion analysis is used to understand public sentiment towards products, services, or events.
  • Sentiment is categorized into positive, negative, and neutral or, contingent outcomes.

Introduction: Opinion Analysis (continued)

  • Sentiment analysis can be applied to various text formats like reviews and social media posts.
  • Sentiment information is analyzed across different entities, relationships, and events to uncover perspectives.

What is an opinion?

  • Opinion is a subjective statement of belief or thought about something.
  • Opinion is dependent on context, background, and culture.
  • Subjective statements vary, so they cannot be proven true or false.

What is an opinion? (continued)

  • An opinion involves a subjective viewpoint about something, and involves an opinion holder and a target.
  • Opinion content or sentiment expression describes the specific opinion.
  • Context clarifies the situation (time, location) in which the opinion was expressed.

Basic Opinion Representation

  • Identify the opinion holder (who expressed the opinion).
  • Determine the opinion target (the subject of the opinion).
  • Define the opinion content (the expressed sentiment).
  • Specify the opinion context (time, location).
  • Identify the opinion polarity (positive, negative, or neutral).

Product Review Example

  • Example: "Peter (iPhone 15): It is too expensive".
  • Opinion Holder: Peter
  • Opinion Target: iPhone 15
  • Opinion Content: "is too expensive"
  • Opinion Context: 2023
  • Opinion Polarity: negative

Opinion Types in Text Data

  • Opinion types include author's opinion, reported opinion, and indirect/inferred opinion.
  • Opinions can reflect the real world.
  • Observed world, perception(perspective), expression are all parts of identifying types of opinions.

Opinion Mining Task

  • The process of extracting opinion representations from text data
  • This involves identifying opinion holders, targets, content, context, and sentiment.

Sentiment Analysis Levels

  • Sentiment analysis can be conducted at different levels (document, sentence, phrase, aspect).
  • Each level provides a different granularity of sentiment analysis.
  • Document-level analysis produces a single polarity rating for the entire document.
  • Sentence-level analysis focuses on the sentiment of individual sentences.
  • Phrase-level analysis focuses on specific phrases to discern sentiment.
  • Aspect level analysis, targets within a sentence, and breaks down sentiment by component or aspect.

Sentiment Analysis Tasks

  • Tasks include polarity and subjectivity classification.
  • Further tasks include aspect-based sentiment analysis (ABSA), sentiment summarization, and sentiment visualization.

Applications

  • Sentiment analysis is used in business intelligence for forecasting prices.
  • It's used in recommendation systems to provide insights into customer preferences.
  • Governments use sentiment analysis to gauge public opinion and track social movements.
  • Businesses use in healthcare and medical domains to understand patient experience.

Demos

  • Text2data, Dandelion, Huggingface, and Monkelearn are some sentiment analysis demos.

General Procedure of Sentiment Analysis

  • Data collection and extraction methods include APIs, available datasets (like Stanford Sentiment Treebank), and web scraping.

General Procedure of Sentiment Analysis (continued)

  • Data preprocessing stages, like tokenization, stop word removal, abbreviation expansion, part-of-speech tagging, and lemmatization, are crucial steps.
  • Feature extraction is a key stage. It involves extracting features such as terms presence (e.g., unigrams, bigrams, trigrams) and their frequency TF-IDF).

Sentiment Analysis Techniques

  • Sentiment analysis methods include lexicon-based approach (or knowledge-based approach), which relies on pre-existing sentiment lexicons.

Sentiment Analysis Techniques (continued)

  • Machine learning approach is another technique. Different machine learning approaches (supervised, unsupervised, semi-supervised, reinforcement learning) are used.

Deep Learning in Sentiment Analysis

  • Deep learning techniques like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers are used for improving sentiment analysis.

Deep Learning in Sentiment Analysis (continued)

  • Aspect-based sentiment analysis (ABSA) identifies aspects within a text and evaluates the sentiment associated with each.
  • Structured Sentiment Analysis (SSA) models generate sentiment graphs to represent all opinion and sentiment information.
  • Different evaluation metrics, like Targeted F1, are specific to SSA.

References

  • Citations used in the presentation to support the claims made within the slides.

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Sentiment Analysis PDF

Description

Test your knowledge on the fundamentals of sentiment analysis with this quiz. Explore key concepts such as opinion holders, opinion content, and sentiment polarity. Perfect for students or professionals looking to assess their understanding of this important topic.

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