Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels. Text summarization is to describe the text within a few sentences, while sentiment classification can be regarded as a special type of summarization which "summarizes" the text into a even more abstract fashion, i.e., a sentiment class. Based on this idea, we propose a hierarchical end-to-end model for joint learning of text summarization and sentiment classification, where the sentiment classification label is treated as the further "summarization" of the text summarization output. Hence, the sentiment classification layer is put upon the text summarization layer, and a hierarchical structure is derived. Experimental results on Amazon online reviews datasets show that our model achieves better performance than the strong baseline systems on both abstractive summarization and sentiment classification.

A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification

https://digitalcommons.aaru.edu.jo/cgi/viewcontent.cgi?article=1050&context=fcij

A survey on opinion summarization techniques for social media

Health care twitter analysis deals with the health related tweets through sentimental analysis by the patients themselves. The application of sentiment analysis has grown enormously. Its application in health care has great potential to analyze and improve the health of a country. In this paper, we try to analyze health tweets for Depression, Anxiety from the mixed tweets by using Multinomial Naive Bayes and Support Vector Regression (SVR) Algorithm as a classifier.

Mining Twitter Data for Depression Detection

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.

A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

Text summarization and sentiment classification, in NLP, are two main tasks implemented on text analysis, focusing on extracting the major idea of a text at different levels. Based on the characteristics of both, sentiment classification can be regarded as a more abstractive summarization task. According to the scheme, a Self-Attentive Hierarchical model for jointly improving text Summarization and Sentiment Classification (SAHSSC) is proposed in this paper. This model jointly performs abstractive text summarization and sentiment classification within a hierarchical end-to-end neural framework, in which the sentiment classification layer on top of the summarization layer predicts the sentiment label in the light of the text and the generated summary. Furthermore, a self-attention layer is also proposed in the hierarchical framework, which is the bridge that connects the summarization layer and the sentiment classification layer and aims at capturing emotional information at text-level as well as summary-level. The proposed model can generate a more relevant summary and lead to a more accurate summary-aware sentiment prediction. Experimental results evaluated on SNAP amazon online review datasets show that our model outperforms the state-of-the-art baselines on both abstractive text summarization and sentiment classification by a considerable margin.

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A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification

Online health communities continue to offer huge variety of medical information useful for medical practitioners, system administrators and patients alike. In this work we collect real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them. We propose to perform Summarization of user posts per drug, and come out with useful conclusions for medical fraternity as well as patient community at a glance. Further, we propose to classify the users based on their ‘emotional state of mind’. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about the triad ‘drugs-symptoms-medicine’ is done by Association Rule Mining.

Summarization and sentiment analysis from user health posts

In recent years, the amount of online content has grown in enormous proportions. Users try to collect valuable information about contents in order to find their way to relevant web pages. And a lot of research is going on to collect valuable service usage data and process it using different methods to know their behaviors. Many systems and approaches have been proposed in the literature which tries to get information about the user’s interests by profiling the user. The objective of the paper is to profile users on their specific devices and the web usage patterns based on the keyboard and mouse usage, time spent on the web. By analyzing the usage patterns of various users, we prove that the patterns exhibited by any one user are different from other users.

Uniqueness in User Behavior While Using the Web

Online health communities offer tremendous medical information that could be available to all. However as is the case with other data intensive applications, this information contains hidden patterns which if explored, analyzed and understood could be very useful for administrators, medical practitioner sand patients alike. There are websites where patients express their experiences or side-effects on drugs used. In this work we collect such real time health posts from reputed websites, and perform data mining to determine the various possible associations from these posts. Also, we shall perform knowledge discovery from user posts, whereby useful ‘patterns’ about groups like: disease to disease, disease to drug and drug to symptom are discovered.

Vinod L. Mane

Papers

Summarization and sentiment analysis from user health posts

Uniqueness in User Behavior While Using the Web

Knowledge discovery from user health posts