Neural Network Based Context Sensitive Sentiment Analysis

Context Sensitive Sentiment Analysis

Whether it automatically extracts it from annotated corpora, or it accesses it via subjectivity lexicons, sentiment analysis makes use of knowledge. Knowledge, however, is domain dependent, and validity of facts might change along with context switches. In spite of this, existing sentiment analysis systems are rather static, in that they are insensitive to context. We believe that opinion minin...

Context-Sensitive Twitter Sentiment Classification Using Neural Network

Sentiment classification on Twitter has attracted increasing research in recent years. Most existing work focuses on feature engineering according to the tweet content itself. In this paper, we propose a contextbased neural network model for Twitter sentiment analysis, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors. Experiments on...

Context-Sensitive Lexicon Features for Neural Sentiment Analysis

Sentiment lexicons have been leveraged as a useful source of features for sentiment analysis models, leading to the state-of-the-art accuracies. On the other hand, most existing methods use sentiment lexicons without considering context, typically taking the count, sum of strength, or maximum sentiment scores over the whole input. We propose a context-sensitive lexicon-based method based on a s...

Context-Sensitive Weights for a Neural Network

PhraseRNN: Phrase Recursive Neural Network for Aspect-based Sentiment Analysis

This paper presents a new method to identify sentiment of an aspect of an entity. It is an extension of RNN (Recursive Neural Network) that takes both dependency and constituent trees of a sentence into account. Results of an experiment show that our method significantly outperforms previous methods.