2,860 research outputs found
Sentiment analysis and opinion mining on E-commerce site
Sentiment analysis or opinion mining help to illustrate the phrase NLP
(Natural Language Processing). Sentiment analysis has been the most significant
topic in recent years. The goal of this study is to solve the sentiment
polarity classification challenges in sentiment analysis. A broad technique for
categorizing sentiment opposition is presented, along with comprehensive
process explanations. With the results of the analysis, both sentence-level
classification and review-level categorization are conducted. Finally, we
discuss our plans for future sentiment analysis research.Comment: 5 pages, 6 figures, 4 table
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
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