Use of LDA Topics in Aspect and Sentiment Analysis by: Masha Igra - PDF document

כ"ז/רייא/עשת"ג Use of LDA Topics in Aspect and Sentiment Analysis by: Masha Igra Adviser: Prof. Michael Elhadad 1 Agenda • Introduction • Previous work – Knowledge Sources for Sentiment Analysis – Two-phase Approach • Aspect Detection • Sentiment Analysis – Joint Models • Proposed method • Results • Summary 2 1

כ"ז/רייא/עשת"ג Introduction “What other people think” has always been an important piece of information during decision making. “The restaurant is really pretty inside and everyone who works there looks like they like it. The food is really great. The reason they aren't getting five stars is because of their parking situation.” 3 Introduction “What other people think” has always been an important piece of information during decision making. “ The restaurant is really pretty inside and everyone who works there Positive looks like they like it. Positive The food is really great. Negative The reason they aren't getting five stars is because of their parking situation .” 4 2

כ"ז/רייא/עשת"ג Challenges Can't we just look for words like “great” or “terrible” ? Yes, but ... ... learning a sufficient set of such words or phrases is an active challenge. "This film should be brilliant. It sounds like a great plot, the actors are first grade, and the supporting cast is good as well, and Stallone is attempting to deliver a good performance. However, it can't hold up." Overall sentiment is negative “She runs the gamut of emotions from A to B." No ostensibly negative words occur. 9 Challenges (2) “Read the book.” - Positive or Negative? Sentiment-related indicators are domain-dependent: “Read the book.” - positive for book, “Read the book.” - negative for movie. “Unpredictable” - positive for movie plots, “Unpredictable” - negative for a car's steering Aspect-related opinion words of restaurant domain: “Large.” - positive for screen aspect “Large.” - negative for battery aspect 10 3

כ"ז/רייא/עשת"ג Terminology Opinion : “An opinion is simply a positive or negative sentiment, view, attitude, emotion, or appraisal about an entity or an aspect of the entity from an opinion holder.” [Kim and Hovy, 2004] Domain: “ A domain is a product, service, person, event or organization. ” [Liu and Zhang, 2012] Aspect: “ An aspect is a set of terms characterizing a subtopic or a theme in a given domain, which can be features of products or attributes of services.” [Liu and Zhang, 2012] 11 Why it is important? With the dramatic growth of user generated content comes a corresponding need for automatic tools capable of extracting relevant information for the user from plain text: • Comparing two similar products: – Presentation to the user the aspects in which the products differ. • Automatic recommendations generation: – Based on similarity between products, user reviews, and history of previous purchases. • A summary of the important factors mentioned in the reviews of a product. 12 4

כ"ז/רייא/עשת"ג Agenda • Introduction • Previous work – Knowledge Sources for Sentiment Analysis – Two-phase Approach • Aspect Detection • Sentiment Analysis – Joint Models • Proposed method • Results • Summary 13 Knowledge Sources for Sentiment Analysis In most sentiment analysis approaches, the following features have been used: – Terms and their frequency : • individual words or word n-grams: “great”, “bad”, “so cheap” • TF-IDF weights ( words that are more frequent in a document than expected across all documents are more relevant than words that are frequent across all documents ): N  tf * idf tf * log i i i df i tf i - the number of times term i occurs in document. N - the total number of documents. df i - the number of documents that contain term i . – Part of speech (POS): adjectives, verbs, nouns . – Opinion words and phrases : words that are commonly used to express positive or negative sentiments: • beautiful, good , and amazing (positive) • bad, poor , and terrible (negative) – Negations : “I don’t like this camera” – Syntactic dependency : word dependency-based features, dependency trees. 14 5

כ"ז/רייא/עשת"ג Aspect Sentiment Analysis Approaches • Two-phase approach : – The first phase attempts to extract the aspects of an object that users frequently rate. – The second phase classifies and aggregates sentiment over each of these aspects. • Joint model : The joint model discovers aspects and sentiment simultaneously. 15 Datasets Dataset Number of Number of aspects sentences Restaurants 6 80,000 Hotels 7 49,471 Multi-Domain 4 3,684 DVD 4 2,660 A restaurant review: <Ambience><Negative> “It became impossible to stand and have a drink or any type of conversation .” <Staff><Negative> “After waiting an hour and a half , we were finally seated at 11:00 .” <Food><Negative> “I had a blue cheese burger that was dry and tasteless .” 16 6

כ"ז/רייא/עשת"ג Two-Phase Approach: Aspect Detection • LocalLDA [Brody and Elhadad, 2010] : a method which operates LDA on sentences, rather than documents, and employs a small number of topics that correspond to ratable aspects. • Latent Dirichlet Allocation (LDA) [Blei et al., 2003] : A probabilistic generative model that can be used to estimate the properties of multinomial observations by unsupervised learning. Intuition: to find the latent structure of “topics” or “concepts” in a text corpus, which captures the meaning of the text. 17 Latent Dirichlet Allocation (LDA) - Blei et al. [2003] 18 7

כ"ז/רייא/עשת"ג LDA (2) 19 The LDA model  u u u z 1 z 2 z 3 z 4 z 1 z 2 z 3 z 4 z 1 z 2 z 3 z 4 w 1 w 2 w 3 w 4 w 1 w 2 w 3 w 4 w 1 w 2 w 3 w 4 b • For each document, • Choose u ~Dirichlet(  ) • For each of the N words wn: – Choose a topic z n » Multinomial( u ) – Choose a word w n from p(w n |z n , b ), a multinomial probability conditioned on the topic z n . 20 8

כ"ז/רייא/עשת"ג The LDA model (cont.) document plate topic plate word plate LDA algorithm solution is based on Gibbs sampling 21 LocalLDA • LocalLDA [Brody and Elhadad, 2010] : According to previous research, LDA is not suited to the task of aspect detection in reviews, because it tends to capture global topics in the data, rather than ratable aspects relevant to the review. In order to prevent the inference of global topics and direct the model towards ratable aspects, they treated each sentence as a separate document. “… public transport in London is straightforward. The tube station is about an 8 minute walk … or you can get a bus for £ 1.50 ”. A global topic: London . A local topic: ratable aspect location . Precision Recall Results: Food 82% 85% Service 71% 75% Atmosphere 63% 61% • There are a lot of variation of LDA extension. 22 9

כ"ז/רייא/עשת"ג Two-Phase Approach: Sentiment Analysis • Linguistic heuristics approach [Hatzivassiloglou and McKeown, 1997]: extracting a list of adjectives that have positive and negative meanings. – Conjunctions between adjectives provide indirect information about orientation: • “fair and legitimate”, “corrupt and brutal”. • “but” usually connects two adjectives of different orientations. – Clustering algorithm separates the adjectives into two subsets of different orientation. – Group of words whose members have the highest average frequency are labeled as positive. Input : Wall Street Journal corpus. Output : Positive and negative adjectives. 23 Sentiment Analysis(2) Classifiers based on machine learning showed higher performance than rule-based classifiers. • Word unigram-based model through SVMs [Pang et al., 2002] • Focus only on subjective sentences in the reviews. But the accuracy of their method is less than that of the classifier using full reviews. [Pang and Lee, 2004] Accuracy Full reviews 87.2% Subjective sentences 87.15% 24 10

כ"ז/רייא/עשת"ג Joint Models • Sentence-LDA (SLDA) and Aspect and Sentiment Unification Model (ASUM) [Jo and Oh, 2011] : one sentence tends to represent one aspect and one sentiment. 25 Research questions • Do topic models help in supervised aspect identification and sentiment detection? • We want to compare results across multiple datasets that have been used in previous work but not previously compared. 26 11

כ"ז/רייא/עשת"ג Agenda • Introduction • Previous work – Knowledge Sources for Sentiment Analysis – Two-phase Approach • Aspect Detection • Sentiment Analysis – Joint Models • Proposed method • Results • Summary 27 Methodology – aspect-sentiment example A restaurant review: “The bar was crowded with other people waiting to be seated for their reservations . It became impossible to stand and have a drink or any type of conversation . After waiting an hour and a half , we were finally seated at 11:00 . I had a blue cheese burger that was dry and tasteless .” 28 12