Sentiment Analysis Approach for Brand Reputation Evaluation

1. Sentiment Analysis Approach for Brand Reputation Evaluation De Jong YeongB.Sc. (Hons) in Computing with Software Development

2. Motivation • User Generated Content (UGC), e.g. reviews had increased significantly over the past decades. • Websites like Amazon consists on 30 departments and sells more than 353 million of products. • Products contain significant amount of reviews. • Requires significant amount of time to read through each review and classify them into positive, negative or neutral. • Negative reviews cause unintended consequences to businesses, can be used as a guideline to improve product or services.

3. Introduction • Sentiment Analysis / Opinion Mining to determine the underlying sentiment of either positive, negative or neutral. • Lexicon-based Approach: Dictionary method (SentiWordNet). • Machine Learning: Multinomial Naïve Bayes. • Hybrid Approach: Latent Semantic Analysis + Linear Support Vector Classifier. • Dataset from Kaggle containing 400,000 review of unlocked mobile phone sold on Amazon was chosen.

4. Research Areas

5. Objective

6. Proposed Method

7. Data Pre-processing Data Pre-processing Pre-processed Data Output (CSV File) Raw Data VADER Sentiment Lexicon Sentiment Label

8. Lexicon-Based Approach SentiWordNet Tokenize Review Sentiment Label Processed Data Model Evaluation Label Encoder Labelled Sentiment

9. Machine Learning Approach TF*IDF Tokenize Review Processed Data Results Multinomial Naïve Bayes Labelled Sentiment TF*IDF: Term Frequency - Inverse Document Frequency

10. Hybrid Approach TF*IDF LSA(n = 100) Tokenize Review Processed Data Results Support Vector Machine Labelled Sentiment Reference: Keith, B., Fuentes, E. & Meneses, C., 2017. A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews. Halifax, Canada, KDD.

11. Sprints (21st Jan – 14th April 2019) • Basic Project Setup. • Data Understanding and Cleaning. • Data Preprocessing and Preparing 01. • Data Preprocessing and Preparing 02. • Refactor and Lexicon-based Approach Sentiment Analysis. • Machine Learning Approach Sentiment Analysis. • Hybrid Approach Sentiment Analysis and Model Comparison.

12. Issues Encountered

13. Key Findings 1 • The accuracy of Hybrid Approach was the highest with 81.2% of correctly predicted observation. • The F1 score of Hybrid Approach was the highest, presenting with 70.2% of harmonic mean between precision and recall. • The precision score of the Lexicon-Based Approach was the lowest, giving 54.0% of correctly predicted positive observations.

14. Key Findings 2 • The positive sentiment label in Apple and BlackBerry mobile reviews were higher, compared to the negative and neutral sentiment labels.

15. Conclusions and Future Work • Hybrid Approach to sentiment analysis can effectively used to evaluate brand reviews that benefit businesses. • The underlying sentiment of brand reviews can be evaluated with the use of sentiment classification techniques. • Emoticons and Slang handling may be implemented to improve results of sentiment analysis.

16. Question? Thank You!