Presentation on theme: "Sentiment Analysis An Overview of Concepts and Selected Techniques."— Presentation transcript:
Sentiment Analysis An Overview of Concepts and Selected Techniques
Terms Sentiment A thought, view, or attitude, especially one based mainly on emotion instead of reason Sentiment Analysis aka opinion mining use of natural language processing (NLP) and computational techniques to automate the extraction or classification of sentiment from typically unstructured text
Motivation Consumer information Product reviews Marketing Consumer attitudes Trends Politics Politicians want to know voters’ views Voters want to know policitians’ stances and who else supports them Social Find like-minded individuals or communities
Problem Which features to use? Words (unigrams) Phrases/n-grams Sentences How to interpret features for sentiment detection? Bag of words (IR) Annotated lexicons (WordNet, SentiWordNet) Syntactic patterns Paragraph structure
Challenges Harder than topical classification, with which bag of words features perform well Must consider other features due to… Subtlety of sentiment expression irony expression of sentiment using neutral words Domain/context dependence words/phrases can mean different things in different contexts and domains Effect of syntax on semantics
Approaches Machine learning Naïve Bayes Maximum Entropy Classifier SVM Markov Blanket Classifier Accounts for conditional feature dependencies Allowed reduction of discriminating features from thousands of words to about 20 (movie review domain) Unsupervised methods Use lexicons Assume pairwise independent features
LingPipe Polarity Classifier First eliminate objective sentences, then use remaining sentences to classify document polarity (reduce noise)
LingPipe Polarity Classifier Uses unigram features extracted from movie review data Assumes that adjacent sentences are likely to have similar subjective-objective (SO) polarity Uses a min-cut algorithm to efficiently extract subjective sentences
LingPipe Polarity Classifier Graph for classifying three items.
LingPipe Polarity Classifier Accurate as baseline but uses only 22% of content in test data (average) Metrics suggests properties of movie review structure
SentiWordNet Based on WordNet “synsets” http://wordnet.princeton.edu/ Ternary classifier Positive, negative, and neutral scores for each synset Provides means of gauging sentiment for a text
SentiWordNet: Construction Created training sets of synsets, L p and L n Start with small number of synsets with fundamentally positive or negative semantics, e.g., “nice” and “nasty” Use WordNet relations, e.g., direct antonymy, similarity, derived-from, to expand L p and L n over K iterations L o (objective) is set of synsets not in L p or L n Trained classifiers on training set Rocchio and SVM Use four values of K to create eight classifiers with different precision/recall characteristics As K increases, P decreases and R increases
SentiWordNet: Results 24.6% synsets with Objective<1.0 Many terms are classified with some degree of subjectivity 10.45% with Objective<=0.5 0.56% with Objective<=0.125 Only a few terms are classified as definitively subjective Difficult (if not impossible) to accurately assess performance
SentiWordNet: How to use it Use score to select features (+/-) e.g. Zhang and Zhang (2006) used words in corpus with subjectivity score of 0.5 or greater Combine pos/neg/objective scores to calculate document-level score e.g. Devitt and Ahmad (2007) conflated polarity scores with a Wordnet-based graph representation of documents to create predictive metrics
References 1. http://www.answers.com/sentiment, 9/22/08 http://www.answers.com/sentiment B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” in Proc Conf on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86, 2002. Esuli A, Sebastiani F. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In: Proc of LREC 2006 - 5th Conf on Language Resources and Evaluation, 2006. Zhang E, Zhang Y. UCSC on TREC 2006 Blog Opinion Mining. TREC 2006 Blog Track, Opinion Retrieval Task. Devitt A, Ahmad K. Sentiment Polarity Identification in Financial News: A Cohesion-based Approach. ACL 2007. Bo Pang, Lillian Lee, A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p.271-es, July 21-26, 2004.