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Opinion mining on experience feedback: A case study on smarphones reviews

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Through the development of electronic commerce, social media and collaborative media, the social commerce appeared. Social commerce, a subset of electronic commerce, is based on social interactions in order to buy and sell goods and services. Nowadays, before buying, people give more importance to the experience feedback they found on internet. However, it is difficult to get an overview of this experience feedback since it is scattered in many online resources, and buyers never have time to read many pages of comments. In this paper, we present an approach which grabs and analyzes experience feedback in order to publish a summary of opinions about a product. We develop this approach with a case study on smartphones and publish a dataset of thousands of comments on a wide range of smartphones. To summarize experience feedback, we use a linguistic appraisal model, based on appreciation, affect and judgement, and we set up an approach using methods and tools from the fields of natural language processing, opinion mining and sentiment analysis.

Publié dans : Ingénierie
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Opinion mining on experience feedback: A case study on smarphones reviews

  1. 1. Opinion mining on experience feedback A case study on smartphones reviews Laurent Brisson & Jean-Claude Torrel IEEE RCIS 2015, May 13-15 2015, Athens
  2. 2. Opinion mining and sentiment analysis Context People give more importance to the experience feedback they found on internet Social interactions have a strong and immediate impact on purchase behaviour There is a huge amount of experience feedback available on internet Tasks Document level: “What’s the overall opinion about this organization in latest news?’ Sentence level: “What are the positive tweets about this film?’ Aspect level: “My clients are they satisfied with the food and service?” 2/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  3. 3. Motivations Objective: To qualifiy experience feedback from customer reviews Constraints: Customer reviews could be about anything The whole analysis process must be deployed in a short period: • No ressource for annotating a learning dataset • No supervised learning algorithms 3/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  4. 4. A 3-step approach to find relevant opinions Knowledge base Sentence pre- processing Textual data Search strategies based on a Linguistic framework for appraisal Opinions A knowledge base to define a relevant perimeter Sentence pre-processing to add information A linguistic framework to structure search strategies 4/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  5. 5. First step: A knowledge base Knowledge base Sentence pre- processing Textual data Search strategies based on a Linguistic framework for appraisal Opinions The knowledge base is composed of: An aspect-lexicon which defines the relevant perimeter to extract opinions Sentic* polarity lexicon to access word polarity *E. Cambria and A. Hussain. Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Cham, Switzerland: Springer (2015) 5/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  6. 6. Second step: Sentence pre-processing Knowledge base Sentence pre- processing Textual data Search strategies based on a Linguistic framework for appraisal Opinions Lexical analysis and transformations Character cleaning Full stop detection Tokenization Constituency-based syntactic analysis S VP NP NN problem JJ only DT the VBZ is NP NN camera DT The 6/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  7. 7. Third step: Search strategies Knowledge base Sentence pre- processing Textual data Search strategies based on a Linguistic framework for appraisal Opinions In order to extract opinions: A breadth-first search is performed on the parse tree Automata detect appraisal patterns defined in the linguistic framework If a pattern is found, a specific rule is applied to extract the opinion 7/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  8. 8. Linguistic framework for appraisal The language of evaluation, James R. Martin and Peter R. R. White Appraisal p. 34 attitude p. 45 affect p. 45 appreciation p. 56 judgement p. 52 engagement p. 97 graduation p. 137 Affect as: • a mental process: “I like this phone” • a mental state: “I’m very happy with this phone” Appreciation as: • qualification: “The camera is the only problem” • presence/absence: “This phone hasn’t got a camera” • behavior: “The battery heats up a lot” Judgement deals with attitudes towards behaviour (social esteem or social sanction) 8/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  9. 9. Search strategies: an example “The camera is the only problem” (Appreciation by qualification) S VP NP NN problem JJ only DT the VBZ is NP NN camera DT The γ 1 NP camera γ 0 γ 2 γ 3 MD MD RB VB VB γ 0 γ 4 γ 5 only γ 6 problem γ 7 RB DT RB, DT NN JJ ADJP, ADVP NN NN ADJP, ADVP JJ NN γC PP NP NP, PP γB VP VP 9/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  10. 10. Experiment Our dataset contains: 40,160 comments about 382 smartphones in 81,431 sentences for a total size of 3.5 Mo. 368 technical specifications from 8 manufacturer websites. A knowledge base with keyword for smartphone’s related concepts. Our validation set contains : 708 opinions in 527 comments about 6 smartphones. Annotations are a consensus between 3 reviewers. They are available for download: http://goo.gl/YCC5We 10/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  11. 11. Results Dataset Appreciation Detection Detection with Polarity Eval. Precision Recall F-Measure Precision Recall F-Measure Galaxy S5 0.87 0.62 0.72 0.82 0.59 0.69 HTC Desire 310 0.90 0.50 0.64 0.82 0.48 0.60 HTC One (M8) 0.82 0.43 0.56 0.80 0.43 0.56 iPhone 6 0.83 0.50 0.62 0.77 0.47 0.59 Lumia 1320 0.90 0.68 0.78 0.86 0.68 0.76 XPERIA C 0.87 0.66 0.75 0.76 0.62 0.68 Average 0.87 0.57 0.68 0.81 0.55 0.65 Good average precision to find appreciation and qualify their polarity Recall have to be improved to find more opinions: • Affect extraction is not yet implemented • Constituency-based syntactic analysis cannot handle complex sentences 11/13 Laurent Brisson & Jean-Claude Torrel Opinion mining on experience feedback - RCIS 2015
  12. 12. Future work Knowledge base Sentence pre- processing Textual data Search strategies based on a Linguistic framework for appraisal Opinions Manage context-based knowledge Handle complex sentences with dependency-based syntactic analysis Find new rules fitted to affect, judgement and engagement 12/13 Laurent Brisson & Jean-Claude Torrel Future work
  13. 13. Conclusion Knowledge base Sentence pre- processing Textual data Search strategies based on a Linguistic framework for appraisal Opinions Semantic ressources to define a semantic relevant perimeter NLP techniques for data preparation and syntactic analysis Use of a sound language framework for appraisal in English Search strategies to find relevant opinions with their polarity 13/13 Laurent Brisson & Jean-Claude Torrel Conclusion

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