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|dc.description.abstract||Contemplating the fact that business enterprises globally, benefit from social media to gain profits, this dissertation discusses the importance and impact of analyzing mechanisms of customer responses on social media to maximize the gained profit and take befitting future decisions. The main objective is to applying sentiment analysis techniques on the collected responses to analyze them and extract the customer feedback. This study focusses on finding out the best model for emotion categorization of the collected responses using the sentiment analysis techniques. The collected responses are preprocessed and undergoes several techniques to achieve data normalization which enable fast and effortless querying. Cleansed data is analyzed in order to place the comments under six main emotion categories related to sentiment analysis. This analysis mainly consists with feature extraction and training the models. The two built-in methods available in scikit learn toolkit, namely; TfidfVectorizer() and CountVectorizer() are used for the feature extraction using both mono-gram and bi-gram features and those extracted features are used to train the selected four (04) classification models in order to find out the best performing model with the four (04) feature types extracted. Subsequently, this study will investigate and compare different features for the different classifier when categorizing emotions on social media. Then record the accuracy of each model with each feature type and select the best performing model with the accuracy of 68% with a 0.1 train-test accuracy difference.||en_US|
|dc.title||ii Analysis of social media feedback to gain profit for business organizations using sentiment analysis techniques||en_US|
|Appears in Collections:||2020|
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|2017 MCS 096.pdf||778.25 kB||Adobe PDF||View/Open|
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