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Customer satisfaction monitoring with sentiment analysis based on twitter feeds in telecom domain

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dc.contributor.advisor Premaratne, SC Chamara, APLDS 2019-02-18T22:45:29Z 2019-02-18T22:45:29Z
dc.description.abstract With this increased competition among telecom service providers, it has become more difficult to retain the existing customers, but when the number of customers reaches its peak, finding and securing new customers become increasingly difficult and costly. Therefore, it would be better to prioritize the retention of the existing customers, than trying to win new ones. Customer reviews can be recognized as fruitful information sources for monitoring and enhancing customer satisfaction levels as they convey the real voices of actual customers expressing relatively unambiguous opinions. This research is aimed at mining and measure customer satisfaction toward Telecom Service based on reviews and feedbacks from Twitter. This research is mainly focus on one of the largest mobile operator in Sri Lanka and the analysis has been done only for English language. Tweets were classified into three classes as Positive, Negative and Neutral with the use of four dictionaries (Lexicon, SentiWordNet, Slangs& Emoticons). The framework was built based on six steps and it shows that Lexicon performs well on the dataset better than SentiWordNet. After fine-tuning lexicon and stop words dictionary and integrating with Slangs dictionary, positive classification shows 91.98% accuracy without Emoticon dictionary while for negative classification, the accuracy is 82.27% with Emoticons dictionary. en_US
dc.language.iso en en_US
dc.subject Twitter Feeds en_US
dc.subject Telecom Industry en_US
dc.subject Sentiment Analysis en_US
dc.subject Lexicon en_US
dc.title Customer satisfaction monitoring with sentiment analysis based on twitter feeds in telecom domain en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty IT en_US Master of science in Information Technology en_US
dc.identifier.department Department of Information Technology en_US 2018
dc.identifier.accno TH3569 en_US

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