2 research outputs found
Multi-Modal Topic Sentiment Analytics for Twitter
Title from PDF of title page viewed February 1, 2019Thesis advisor: Yugyung LeeVitaIncludes bibliographical references (pages 50-53)Thesis (M.S.)--School of Computing and Engineering, University of Missouri--Kansas City, 2018Sentiment analysis has proven to be very successful in text applications. Social media
is also considered a quite rich source to get data regarding user’s behaviors and
preference. Identifying social context would make the sentiment analysis more meaningful
to the applications. Due to the limited contextual information in social media, it would be
quite challenging to conduct context-aware sentiment analysis with social media. Promising
frameworks such as CoreNLP, Text Blob, and Vader have been introduced to identify
sentiments in the text. However, it seems to not be adequate to contextual sentiment
analysis in social media like Twitter.
In this thesis, we present a contextual sentiment framework that is designed
to leverage the power of the multiple models in the social context. The framework aims to
classify contextual sentiment from the Twitter data as well as to discover hidden trends and
topics (context) using topic modeling techniques like Latent Dirichlet Allocation (LDA). We
have focused on the mismatch cases among multiple models in which different experts
(models) have different opinions on social media sentiments. We have identified the five
mismatch types in the social sentiment through the analysis of diverse experiments ( human
machine model, and machine-machine model). We have implemented the mismatch
detection among the three models (i.e., Vader, Text Blob, and CoreNLP) and automatically
corrected them by applying semantic rules to sentiment models. We compared our
approach against a traditional single model approach concerning a performance metric
(accuracy) and Kappa (evaluating consensus among multi-models) on three benchmarks
datasets and our dataset we collected from a health dieting domain. The proposed
framework showed notable performance improvement in comparison with the traditional
one concerning both evaluation metrics.Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future wor