Motivation Sentiment Analysis of Product Reviews

Introduction

• Introduction

• Motivation

• Sentiment Analysis of Product Reviews

• Preparation of Dataset
• Learning
• Processing of Product Reviews
• Learning Association Rules
• Presentation of Results

• Progress So Far

• Summary

• Conclusion

User participation to Web sites increased with Web 2.0

• User participation to Web sites increased with Web 2.0

• Product reviews written by users in e-commerce sites

• User opinions

• Essential as they reflect the real experience of the people who actually use the products

Use opinion mining (sentiment analysis)

• Use opinion mining (sentiment analysis)

• Derive user opinions about product features
• Determine their sentiment orientation
• Analyzing if an opinion is positive or negative
• Summarize that information to the user

• Dataset

• Use Turkish product reviews for mobile phones

Influence of the experience of a product’s users on people who consider buying it

• Influence of the experience of a product’s users on people who consider buying it

• Their analysis will be useful for buyers, producers and e-commerce systems

• Users start to read a small fraction of product reviews as the number of them in e-commerce systems increases

• Usually results in unawareness of some features of the products and opinions about them

• Product reviews are generally repetitive

• Reading all of them is generally inefficient
• There is a need for summarization in product reviews

• Lack of such a system for Turkish language

It consists of five steps

• It consists of five steps

• Preparation of Dataset
• Learning
• Processing of Product Reviews
• Learning Association Rules
• Presentation of Results

Use mobile phone reviews in Hepsiburada.com

• Use mobile phone reviews in Hepsiburada.com

• Choice is based on the size of the dataset provided

• Parse the website

• To find links to cell phones
• To extract user reviews

• Strip off text from HTML tags

• Put the parsed text into a database with some extra information

• Reviewer’s grade of the product
• People’s grade of the review etc

Calculate sentiment orientation of words

• Calculate sentiment orientation of words

• Using Word Net with seeded oriented words and Turney’s approach using search engine queries are not suitable for Turkish

• Best approach so far is using the reviewer’s grade of the product

• For each opinion word owj

• Orientation (owj) = ∑ (tfi,j x idfj x gi) / |{r:owj Є r}|

Calculation of likelihood of feature - opinion match

• Calculation of likelihood of feature - opinion match

• For each sentence

• Find feature and opinions
• Count number of times they appear together
• Count their individual appearances

• Calculate likelihood of feature opinion match

• |Featurei & Opinionj|2 / |Featurei| x |Opinionj|

Aims to find
and matches

• Aims to find
  and matches

• Example

• “Fiyatına göre iyi bir telefon kullanışlı tavsiye ederim.”
• Features: telefon, fiyat
• Opinions: iyi, kullanışlı, tavsiye ederim
• Matches: , ,

First thing to do is applying POS Tagger to a sentence

• First thing to do is applying POS Tagger to a sentence

• “Konuşurken karşı tarafın sesi sanki biraz az geliyor gibi geldi bana.” → “Adverb Adj Noun+A3sg+Pnon+Gen Noun+A3sg+P3sg+NomFet Adj Adj Adj Verb+Pos+Prog1+A3sg Postp Verb+Pos+Past+A3sg Pron+A1sg+Pnon+Dat Punc“

• For opinion finding, we only use adjectives, we miss some opinions words like “tavsiye ediyorum”

• For features, we search them from a list we have

• “Kamerası iyi çekiyor.” (explicit feature : kamera)
• “Telefon çekim kalitesi yüksek.” (implicit feature: kamera?)

Assignment of opinions to features

• Assignment of opinions to features

• Use rules
• (Adv) Adj (Num) Noun, Noun (Adv|Adj) Adj Punc
• Use Likelihood values
• Find assignment among feature and opinions that maximize the sum of likelihoods which has been learned earlier in learning process.

• Store features, feature-opinion pairs and their places that are mentioned in product

Perform association rule analysis to obtain frequent feature item sets

• Perform association rule analysis to obtain frequent feature item sets

• Use transactions extracted in the previous step

• Association rule

• Implication in the form of X => Y
• Existence of variable X implies existence of Y
• Two kinds of association rules
• Product => Feature
• Feature => Opinion

• After obtaining such association rules, prune the ones that are not repeated frequently and ones that are not interesting regarding their sentiment orientation

Provide a web user interface

• Provide a web user interface

• Users can access the results by submitting the name of the product they want to fetch information about to the system

• Example Interface

Accomplished most of the essential steps of our project

• Accomplished most of the essential steps of our project

• Prepared our dataset
• Fetch data from Hepsiburada.com
• Process it
• Put it into a database
• Performed sentiment analysis
• Obtained promising results with our methods

• Now, we are working on our web user interface and processing of product reviews

Project’s five steps

• Project’s five steps

• Preparation of Dataset
• Learning
• Processing of Product Reviews
• Learning Association Rules
• Presentation of Results

Problems

• Problems

• Authors don’t use the language properly and correctly
• There is no tool to perform syntax analysis of Turkish
• Evaluation problem: How to calculate recall?

• Simple solutions generally work better in diverse datasets and high dimensional problems