![]() The framework mainly contains three modules: The review helpfulness prediction module, topic-sentiment modeling module, and multi-factor ranking module. In the paper, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. ![]() Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously. There exist prior explorations on app review mining for release planning, however, most of the studies strongly rely on pre-defined classes or manually-annotated reviews. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.Īpp reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. We carry out extensive experiments on real-world short text collections. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). ![]() The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. LDA and PLSA) on such short texts may not work well. ![]() However, directly applying conventional topic models (e.g. Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. ![]()
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January 2023
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