13 research outputs found
Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM
Sentiment analysis on large-scale social media data is important to bridge
the gaps between social media contents and real world activities including
political election prediction, individual and public emotional status
monitoring and analysis, and so on. Although textual sentiment analysis has
been well studied based on platforms such as Twitter and Instagram, analysis of
the role of extensive emoji uses in sentiment analysis remains light. In this
paper, we propose a novel scheme for Twitter sentiment analysis with extra
attention on emojis. We first learn bi-sense emoji embeddings under positive
and negative sentimental tweets individually, and then train a sentiment
classifier by attending on these bi-sense emoji embeddings with an
attention-based long short-term memory network (LSTM). Our experiments show
that the bi-sense embedding is effective for extracting sentiment-aware
embeddings of emojis and outperforms the state-of-the-art models. We also
visualize the attentions to show that the bi-sense emoji embedding provides
better guidance on the attention mechanism to obtain a more robust
understanding of the semantics and sentiments
Feature selection for ordinal regression
Ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. This problem is receiving increasing attention from the sentiment analysis and opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. As in other supervised learning tasks such as (binary or multiclass) classification, feature selection is needed in order to improve efficiency and to avoid overfitting. However, while feature selection has been extensively studied for other classification tasks, is has not for ordinal regression. In this paper we present four novel feature selection metrics that we have specifically devised for ordinal regression, and test them on two datasets of product review data