Link to slide: SlideServe
- Can computers perform reputation evaluation that benefit businesses?
- How sentiment analysis can be used to evaluate reputation of a product or services?
- Evaluate and Compare the selected sentiment classification techniques used to evaluate brand reviews.
- Findings are presented to make informative decisions regarding the adoption of classification techniques.
- Dataset containing 400,000 reviews of unlocked mobile phones sold on Amazon was selected.
- Three approaches (lexicon-based, machine learning and hybrid) were implemented to identify the underlying sentiment.
- Model evaluation metrics were utilised for comparative analysis.
- PyCharm by JetBrains
- Python
- Scikit-Learn Library
- Accuracy of Hybrid approach was the highest, giving 81.2% of correctly predicted observation.
- Precision score of Lexicon-based approach was the lowest with 54.0% of correctly predicted positive observations.
- F1 score of Hybrid approach was the highest, presenting with 70.2% of harmonic mean between precision and recall.
- The positive sentiment label in Apple and BlackBerry mobile reviews were higher, compared to the negative and neutral sentiment labels.
- Hybrid approach to sentiment analysis can effectively be used to evaluate brand reviews that benefit businesses.
- Underlying sentiment of brand reviews can be evaluated with the use of sentiment classification techniques.
- Slang and emoticons handling may be implemented to improve results of sentiment analysis.