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https://github.com/dejongyeong/sentiment-analysis

Final Year Project: Sentiment Analysis Approach for Reputation Evaluation
https://github.com/dejongyeong/sentiment-analysis

hybrid-approach lexicon-based machine-learning naive-bayes-classifier python scikit-learn sentiment-analysis support-vector-machines

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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](https://www.slideserve.com/dejongy/sentiment-analysis-approach-for-brand-reputation-evaluation-powerpoint-ppt-presentation)

### 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.