Main Reference PaperTASC:Topic-Adaptive Sentiment Classification on Dynamic Tweets, IEEE Transactions on Knowledge and Data Engineering, June 2015
  • This work focuses on sentiment analysis on tweets, because it suffer from the problems of lack of adapting to unpredictable topics and labeled data, and extremely sparse text. Proposed work implements a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics and selected data to adapt the sentiment classifier to the unlabeled data including topic related sentiment words and sentiment connections derived from “@” mentions of tweets, named as topic adaptive features. An enhanced of TASC-t is designed to adapt along a timeline for the dynamics of tweets.

+ Description
  • This work focuses on sentiment analysis on tweets, because it suffer from the problems of lack of adapting to unpredictable topics and labeled data, and extremely sparse text. Proposed work implements a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics and selected data to adapt the sentiment classifier to the unlabeled data including topic related sentiment words and sentiment connections derived from “@” mentions of tweets, named as topic adaptive features. An enhanced of TASC-t is designed to adapt along a timeline for the dynamics of tweets.

  • To solve the problem of sentiment classification on tweets.

  • To improve the sentiment classification using TASC model.

  • To adapt the sentiment classification dynamics of tweets.

+ Aim & Objectives
  • To solve the problem of sentiment classification on tweets.

  • To improve the sentiment classification using TASC model.

  • To adapt the sentiment classification dynamics of tweets.

  • A technique is contributed to improve the accuracy of sentiment classification on dynamic tweets.

+ Contribution
  • A technique is contributed to improve the accuracy of sentiment classification on dynamic tweets.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 & J2SE

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 & J2SE

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

  • 10 – 15 Days

+ Order To Delivery
  • 10 – 15 Days

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