Abstract
Interactive sentiment analysis is an emerging, yet challenging, subtask of the natural language processing problem. It aims to discover the affective state and sentimental change of each person in a conversation, and has attracted an increasing attention from both academia and industry. Existing sentiment analysis approaches are insufficient in modelling the interactions among people. However, the development of new approaches are critically limited by the lack of labelled interactive sentiment datasets. In this paper, we present a new conversational database that we have created and made publicly available, namely ScenarioSA, for interactive sentiment analysis. We manually label 2,214 multi-turn English conversations collected from various websites that provide online communication services. In comparison with existing sentiment datasets, ScenarioSA (1) is no longer limited to one specific domain but covers a wide range of topics and scenarios; (2) describes the interactions between two speakers of each conversation; and (3) reflects the sentimental evolution of each speaker over the course of a conversation. Finally, we propose an extension of interactive attention networks that could model the interactions, and compare various strong sentiment analysis algorithms on ScenarioSA, demonstrating the need of novel interactive sentiment analysis models and the potential of ScenarioSA to facilitate the development of such models.
Highlights
Sentiment Analysis (SA) has been a core research topic in Natural Language Processing (NLP)
EVALUATION WITH ScenarioSA Note that this paper focuses on presenting an interactive sentiment dataset, demonstrating the need of novel interactive sentiment analysis models and the potential of the dataset to facilitate the development of such models, rather than model designing
We propose an extension of interactive attention networks, and evaluate several strong sentiment analysis methods over the ScenarioSA collection, checking whether existing sentiment analysis models could effectively solve interactive sentiment analysis problem or not
Summary
Sentiment Analysis (SA) has been a core research topic in Natural Language Processing (NLP). It aims at discovering diversified subjective information implied in the given natural language text [1]. Most existing sentiment analysis approaches focus on identifying the polarity of commentaries or similar type of texts (i.e. movie reviews, product reviews and twitter posts) at the document-, sentence- or. The commentary documents used in these studies are in the form of individual narratives, without involving interactions among the writers or speakers. A large volume of interactive texts have been produced, which carry rich subjective information [4]. Recognizing the polarity of the interactive texts and its evolution with respect to people’s interaction is of a great theoretical and practical significance.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
