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Tutorial on Opinion Mining and Sentiment Analysis

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Образец заголовка Tutorial on Opinion Mining and Sentiment Analysis by Rezvaneh Rezapour (rezapou2) and Yun Hao (yunhao2) Prepared as an assignment for CS410: Text Information Systems in Spring 2016
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Page 1: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовка

Tutorial on Opinion Mining and Sentiment

Analysisby Rezvaneh Rezapour (rezapou2) and Yun Hao (yunhao2)

Prepared as an assignment for CS410: Text Information Systems in Spring 2016

Page 2: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаIntroduction• How do you choose a movie to

watch?• How do you pick a restaurant or

hotel?• How do you decide which camera to

buy?

Fig 1

Page 3: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаIntroduction

Fig 2

Fig 3 and Fig 4

Fig 5

Page 4: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаIntroduction• People like to share their experiences or opinions

about a place, event or product with others.• There are numerous Web sites and pages

containing consumer opinions, for example Amazon and IMDB are great and valuable sources of information (reviews) to find other’s opinions.

• This online word-of-mouth behavior represents new and measurable sources of information. [10]

• But……. It is tooooo much !!!!!!!

Page 5: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаMotivation• What do we need ?– Study and extract useful information

from individuals’ reviews.

• Why is it helpful?– Save time– Help to find good and bad features– Help to find positive and negative points

Opinion Mining

Sentiment Analysis

Page 6: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаOpinion Mining• Definition– If a set of text documents (T) are given,

that have opinions on an object, opinion mining intends to identify attributes of the object on which opinion have been given, in each of the document and to find orientation of the comments i.e. whether the comments are positive or negative. [8]

Page 7: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаOpinion Mining (cont.)• Some terms are often used

interchangeably for opinion mining.

Fig 6 Synonyms of Opinion Mining [8]

Page 8: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаComponents of Opinion Mining Model

• Question: What do we want to extract from a review?– Positive and Negative opinions– Target of the opinions; Entity – Related set of components; aspect– Related attributes; aspect– Sometimes opinion holder; opinion

source

iPhoneBattery

Voice quality

Page 9: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовка• Question: What do we want to

extract from a review?– Positive and Negative opinions– Target of the opinions; Entity – Related set of components; aspect– Related attributes; aspect– Sometimes opinion holder; opinion

source

Object

Features

Opinion Holder

Opinion Passage on a Feature

Components of Opinion Mining Model(cont.)

Page 10: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаOpinions• Regular: usually referred to as opinion

• Positive or Negative sentiment, attitude or appraisal about an entity or aspect

• Comparative:• Relation of similarities or differences between two or

more entities• Preference of opinion holder based on shared aspects• Usually consists of comparative or superlative

adjectives or adverbs• Need to first identify the objects being compared, the

features being compared, and the preferences of the comparison [8]

Page 11: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаSubjectivity and Emotion• Objective sentence present factual

information• Subjective sentence present feelings

and beliefs• Emotions are subjective feelings and

thoughts• Some sentences express no emotion

or opinion

Page 12: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаOpinion Summary• Aspect Based:– Highlight important parts of the reviews– Produce a short text summary

Phone 1: Aspect: General

Positive: 5 <Sentence>Negative: 3 <Sentence>

Aspect: BatteryPositive: 20 <Sentence>Negative: 5 <Sentence>

Pros: Easy to read and understandCons: very qualitative

Page 13: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаChallenges and Issues• Challenges

– Relevant objects vs irrelevant ones– Same feature expressed in different wordings– Words that could be positive and negative in different context– Long text that could contain both positive and negative opinions– Detecting opinion oriented sentences– Integrating the tasks above

• Some other issues– Identifying comparison words– Dealing with different writing style by different people– Tracking changing opinions– Measuring strength of opinions– Tackling sarcastic statements and mixed views– Spam opinions

Page 14: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаSentiment Classification• Unit of Analysis: – Sentence– Document

• Methods:– Supervised– Semi-Supervised– Unsupervised

Page 15: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаGetting Entity and Opinion• Create a structured text from reviews– Extract object features and opinions– Determine all sentiment polarities for opinions– Determine relevant opinions for each object

features• Method:– Use Conditional random Fields

• Linear CRFs ; Computed MAP– Leverage conjunction structure and syntactic

tree structure and integrate them both.

Page 16: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаGetting Entity and Opinion (cont.)

• What features?– Token, lemma, part of speech– Expand each word by getting synonyms and

antonyms from WordNet– Use SentiWordNet to get the prior polarity

• Create your baseline– Rule-Based methods– Lexicon-Based methods

• Finding of the related paper [2]:– The proposed framework in the paper

outperformed many state-of-art methods.

Page 17: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаUsing Ontology to Identify Feature

• How?– Use a seed set from the reviews

• Use ontology construction to:– Select relevant sentences including

conceptions– Extract the conceptions from those

sentences• Sentences should consist of conjunctions

and at least one concept seed.

Page 18: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаUsing Ontology to Identify Feature (cont.)

• Feature identification:– Use ontology terminologies to extract features

• Identify related sentences which contains ontology terminologies• Polarity Identification:

– Use SentiWordNet– Calculate a score for positive, negative and neutral words– Generate an adjective lexicon with prior polarities

• Sentiment Analysis:– Calculate the overall opinion– Consider negative words and conjunctions words

• Finding of the related paper[3]:– The experiment was successful and the result is good:

• Accuracy of Feature Detection Result: 76.9%• Accuracy of Polarity Analysis: positive: 88.3% negative: 81.7%

Page 19: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаMaking Use of Other Features

• Hypothesis 1:– Users prefer reviews that satisfies their information need,

that are credible, and that have mainstreaming opinion. [6]

• Features indicating…– Information need

• Whether the review satisfies users’ information need– Credibility

• Is the review credible enough?– Bias

• Is the review one of the mainstreaming ones?

• How to quantify these features?

Page 20: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаMaking Use of Other Features (cont.)

• How to quantify these features? [6]

– Information need• Capture rate: the ratio of words in product attributes and functions

mentioned in the content of reviews – Credibility

• Reliable writers often use past and perfect tense in their writing according to psychological theory.

• The percentage of volitive auxiliary in a review and the percentage of past and perfect tenses in a review.

– Bias• The most frequent in reviews for a product is considered as

mainstreaming opinion (based on data from Amazon), and reviews that are given the same number of stars for the product is considered to carry mainstreaming opinion.

• The divergence (of the ratings) from mainstreaming opinion for a review is calculated.

Page 21: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаMaking Use of Other Features (cont.)

• Hypothesis 2:– Reviews of reasonable length and lacking spelling

and grammar errors are easy to read and thus more helpful. [7]

• Features indicating…– The average level of subjectivity and the range

and mix of subjectivity and objectivity– Content readability

• How to quantify these features?

Page 22: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаMaking Use of Other Features (cont.)

• How to quantify these features? [7]

– The average level of subjectivity and the range and mix of subjectivity and objectivity• An average probability of a review being

subjective (objective information is considered as the information that also appears in the product description, and subjective is everything else)

– Content readability• Number of spelling mistakes within each review• Number of sentences, words, and characters of a

review

Page 23: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаMaking Use of Other Features (cont.)

• Hypothesis 3: – Customer opinions highly depend on the features of the

product being reviewed. [9]

• How to learn useful features from the reviews? [9]

1. Identify the features that are relevant to consumers as regarding to a certain type of product as well as the salience (relative importance of the features)

– Translate text into WordNet concepts and construct a graph with concepts being vertices and “is-a” relation being edges

– Use semantic similarity to add new edges to similar vertices 2. Locate all the related mentions of the identified features

in the reviews3. Quantify opinions mined from the reviews and create a

corresponding numeric vector for each review

Page 24: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаWhat If Opinions Are Hidden?

• Going beyond overall rating to find user’s opinion about different aspects

• How?– Use Latent Aspect Rating Analysis (LARA)

• Approach:– Identify the major aspects and segment reviews

• How? Bootstrapping-based algorithm guided by a few seed words describing the aspects

– Infer aspect ratings and weights for each individual review based on the content and overall rating• How? A generative Latent Rating Regression (LRR) model

Page 25: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаWhat If Opinions Are Hidden? (cont.)

• LRR?– The overall rating is assumed to be generated

from small aspects in the review which can be captured and weighted using a regression model.

– After inferring aspects and their weights we use Maximum Likelihood estimator (using EM algorithm) to find the optimal value that can maximize the probability of observing the overall ratings.

• Finding from related paper[5]:– LRR worked better than the other baseline

algorithms in measuring aspect ratings.

Page 26: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаCan Social Context Help in Review Mining?

• What is social context?– The history of the reviewers and their social network

interactions.• This information is specified to some social network websites and

not all.• Using textual context and social context information can be

helpful in evaluating the quality of individual reviewers and reviews.

• How?– Construct a baseline using labeled reviews and the

review quality pair consists of the quality and helpfulness of each review which comes from manual labeling.

– Improve the above mentioned feature by adding social context.

– Use labeled data, unlabeled data and their social context information to create a semi-supervised model.

Page 27: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаCan Social Context Help in Review Mining? (cont.)

• Features:– Text statistics: e.g.: length of the review, average length of

sentences– Syntactic features: # of POS tags– Conformity features: comparison of the review with other

reviews using KL-divergence.– Sentiment features: positive and negative words in the

reviews.• Extract features and constraints from social context and

add the regularizations to the model.• Finding of the related paper[4]:

– Using regularizations on social context improved the accuracy of the prediction when working with small training data.

Page 28: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаReferences• Images:

Fig1: http://www.necatidemir.com.tr/wp-content/uploads/2015/02/data-answer.jpgFig 2: http://image.slidesharecdn.com/fightclubnetworks-160308231653/95/fight-club-networks-18-638.jpg?cb=1457479147Fig 3: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTY7wEWFCPkJNq96bgRLSljZm_dOf3zY7-THpQ_315cMIF_0FwKFig 4: http://www.diplomatic.lv/sites/default/files/editor/why_choose_uk_insurance_direct_0.gifFig 5: http://www.5wconsulting.com/uploads//over-inflating-your-opinion.gif

• Papers:[1] Liu, B. and L. Zhang (2012). A survey of opinion mining and sentiment analysis. Mining text data, Springer: 415-463.[2] Li, F., C. Han, M. Huang, X. Zhu, Y.-J. Xia, S. Zhang and H. Yu (2010). Structure-aware review mining and summarization. Proceedings of the 23rd international conference on computational linguistics, Association for Computational Linguistics.[3] Zhao, L. and C. Li (2009). Ontology-Based Opinion Mining for Movie. KSEM 2009, LNAI 5914, pp 204-214.[4] Lu, Y., Tsaparas, P., Ntoulas, A., & Polanyi, L. (2010). Exploiting social context for review quality prediction. Paper presented at the Proceedings of the 19th international conference on World Wide Web.[5] Wang, H., Lu, Y., & Zhai, C. (2010). Latent aspect rating analysis on review text data: a rating regression approach. Paper presented at the Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining.

Page 29: Tutorial on Opinion Mining and Sentiment Analysis

Образец заголовкаReferences• Papers (cont.):

[6] Hong, Y., Lu, J., Yao, J., Zhu, Q., & Zhou, G. (2012, August). What reviews are satisfactory: novel features for automatic helpfulness voting. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (pp. 495-504). ACM.[7] Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. Knowledge and Data Engineering, IEEE Transactions on, 23(10), 1498-1512.[8] Seerat, B., & Azam, F. (2012). Opinion mining: Issues and challenges (a survey). International Journal of Computer Applications, 49(9). [9] de Albornoz, J. C., Plaza, L., Gervás, P., & Díaz, A. (2011). A joint model of feature mining and sentiment analysis for product review rating. In Advances in information retrieval (pp. 55-66). Springer Berlin Heidelberg.[10] Liu, B., & Chen-Chuan-Chang, K. (2004). Editorial: special issue on web content mining. ACM SIGKDD Explorations Newsletter, 6(2), 1-4.


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