Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis
نویسندگان
چکیده
منابع مشابه
Tandem LSTM-SVM Approach for Sentiment Analysis
English. In this paper we describe our approach to EVALITA 2016 SENTIPOLC task. We participated in all the subtasks with constrained setting: Subjectivity Classification, Polarity Classification and Irony Detection. We developed a tandem architecture where Long Short Term Memory recurrent neural network is used to learn the feature space and to capture temporal dependencies, while the Support V...
متن کاملSentiment analysis on conversational texts
This paper describes ongoing work related to the analysis of spoken utterance transcripts and estimating the speaker’s attitude towards the whole dialogue on the basis of their opinions expressed by utterances. Using the standard technology used in sentiment analysis, we report promising results which can be linked to the conversational participants’ self-evaluation of their experience of the i...
متن کاملSpoken Conversational Interaction for Language Learning
This paper describes our efforts towards utilizing multilingual spoken dialogue systems as an aid to second language acquisition. We argue that it is important for language students to have the opportunity to practice communication in a non-threatening environment, something that a computer can naturally provide. We envision a three-stage interaction focused around a specific topic of a lesson ...
متن کاملLinguistically Regularized LSTM for Sentiment Classification
This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which has remarkably degraded performance when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words,...
متن کاملModeling Rich Contexts for Sentiment Classification with LSTM
Sentiment analysis on social media data such as tweets and weibo has become a very important and challenging task. Due to the intrinsic properties of such data, tweets are short, noisy, and of divergent topics, and sentiment classification on these data requires to modeling various contexts such as the retweet/reply history of a tweet, and the social context about authors and relationships. Whi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Networks
سال: 2021
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2020.10.001