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Final Year Project: Sentiment Analysis Approach for Reputation Evaluation

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Sentiment Analysis Approach for Reputation Evaluation

Link to slide: SlideServe

Research Question:

  • Can computers perform reputation evaluation that benefit businesses?
  • How sentiment analysis can be used to evaluate reputation of a product or services?

Objective

  • 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.

Proposed Methodology

  • 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.

Programming Language and Software Tools

  • PyCharm by JetBrains
  • Python
  • Scikit-Learn Library

Results

  • 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.

Conclusions

  • 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.