109 research outputs found
Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs
Introduction: Microblogging websites have massed rich data sources for
sentiment analysis and opinion mining. In this regard, sentiment classification
has frequently proven inefficient because microblog posts typically lack
syntactically consistent terms and representatives since users on these social
networks do not like to write lengthy statements. Also, there are some
limitations to low-resource languages. The Persian language has exceptional
characteristics and demands unique annotated data and models for the sentiment
analysis task, which are distinctive from text features within the English
dialect. Method: This paper first constructs a user opinion dataset called
ITRC-Opinion by collaborative environment and insource way. Our dataset
contains 60,000 informal and colloquial Persian texts from social microblogs
such as Twitter and Instagram. Second, this study proposes a new deep
convolutional neural network (CNN) model for more effective sentiment analysis
of colloquial text in social microblog posts. The constructed datasets are used
to evaluate the presented model. Furthermore, some models, such as LSTM,
CNN-RNN, BiLSTM, and BiGRU with different word embeddings, including Fasttext,
Glove, and Word2vec, investigated our dataset and evaluated the results.
Results: The results demonstrate the benefit of our dataset and the proposed
model (72% accuracy), displaying meaningful improvement in sentiment
classification performance
Stance Detection in Web and Social Media: A Comparative Study
Online forums and social media platforms are increasingly being used to
discuss topics of varying polarities where different people take different
stances. Several methodologies for automatic stance detection from text have
been proposed in literature. To our knowledge, there has not been any
systematic investigation towards their reproducibility, and their comparative
performances. In this work, we explore the reproducibility of several existing
stance detection models, including both neural models and classical
classifier-based models. Through experiments on two datasets -- (i)~the popular
SemEval microblog dataset, and (ii)~a set of health-related online news
articles -- we also perform a detailed comparative analysis of various methods
and explore their shortcomings. Implementations of all algorithms discussed in
this paper are available at
https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media
Tension Analysis in Survivor Interviews: A Computational Approach
Tension is an emotional experience that can occur in different contexts. This phenomenon can originate from a conflict of interest or uneasiness during an interview. In some contexts, such experiences are associated with negative emotions such as fear or distress. People tend to adopt different hedging strategies in such situations to avoid criticism or evade questions.
In this thesis, we analyze several survivor interview transcripts to determine different characteristics that play crucial roles during tension situation. We discuss key components of tension experiences and propose a natural language processing model which can effectively combine these components to identify tension points in text-based oral history interviews. We validate the efficacy of our model and its components with experimentation on some standard datasets. The model provides a framework that can be used in future research on tension phenomena in oral history interviews
Role of sentiment classification in sentiment analysis: a survey
Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results
Sentiment Analysis on Financial News and Microblogs
Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. This thesis highlights the importance of sentiment analysis, provides a summary of seminal works and different approaches towards sentiment analysis. It aims to address sentiment analysis on financial news and microblogs by classifying textual data from financial news and microblogs as positive or negative. Sentiment analysis is performed by making use of paragraph vectors and logistic regression in this thesis and it aims to compare it with previously performed approaches to performing analysis and help researchers in this field. This approach achieves state of the art results for the dataset used in this research. It also presents an insightful analysis of the results of this approach
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