Proceedings ArticleDOI
Summarization and sentiment analysis from user health posts
Vinod L. Mane,Suja S. Panicker,Vidya B. Patil +2 more
- pp 1-4
Reads0
Chats0
TLDR
This work collects real time health posts from reputed websites, where patients express their views, including their experiences and side-effects on drugs used by them, and proposes to classify the users based on their `emotional state of mind'.Abstract:
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.read more
Citations
More filters
Posted Content
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
TL;DR: This work proposes 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 theText summarization output.
Journal ArticleDOI
A survey on opinion summarization techniques for social media
TL;DR: This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features are shown.
Proceedings ArticleDOI
Mining Twitter Data for Depression Detection
Priyanka Arora,Parul Arora +1 more
TL;DR: This paper tries 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.
Proceedings ArticleDOI
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
TL;DR: A novel dual-view model is proposed that jointly improves the performance of these two tasks, review summarization and sentiment classification, and 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.
Proceedings Article
A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification
Hongli Wang,Jiangtao Ren +1 more
TL;DR: A Self-Attentive Hierarchical model for jointly improving text Summarization and Sentiment Classification (SAHSSC), which outperforms the state-of-the-art baselines on both abstractive text summarization and sentiment classification by a considerable margin.
References
More filters
Posted Content
People on Drugs: Credibility of User Statements in Health Communities
TL;DR: This work proposes a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources and introduces a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity.
Proceedings ArticleDOI
A Context Based Text Summarization System
Rafael Ferreira,Frederico Luiz Gonçalves de Freitas,Luciano Cabral,Rafael Dueire Lins,Rinaldo Lima,Gabriel Franca,Steven J. Simske,Luciano Favaro +7 more
TL;DR: This paper advocates the thesis that the quality of the summary obtained with combinations of sentence scoring methods depend on text subject, and evaluates the validity of the hypothesis formulated and point at which techniques are more effective in each of those contexts studied.
Proceedings ArticleDOI
An approach to automatic text summarization using WordNet
Alok Ranjan Pal,Diganta Saha +1 more
TL;DR: This approach evaluates the weights of all the sentences of a text separately using the Simplified Lesk algorithm and arranges them in decreasing order according to their weights, so that according to the given percentage of summarization, a particular number of sentences are selected from that ordered list.
Proceedings Article
Automatic Text categorization and summarization using rule reduction
C. Lakshmi Devasena,M. Hemalatha +1 more
TL;DR: A text analyzer is developed to derive the structure of the input text using rule reduction technique in three stages namely, Token Creation, Feature Identification and Categorization and Summarization.
Proceedings ArticleDOI
A hybrid model for named entity recognition using unstructured medical text
TL;DR: In the proposed model, a lexicon is first used as the initial step to detect drug named entities andference rules are then deployed to further extract undetected drug names from unstructured and informal medical text.