Large-scale Sentiment Analysis Research Articles

A large-scale sentiment analysis using political tweets

<p><span lang="EN-US">Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC.</span></p>

Who post more negatively on social media? A large-scale sentiment analysis of Weibo users

Who post more negatively on social media? A large-scale sentiment analysis of Weibo users

A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election

We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the differences. Most notably, we show that deleted tweets, posted after the Election Day, were more favorable to Joe Biden, and the ones posted leading to the Election Day, were more positive about Donald Trump. Also, the older a Twitter account was, the more positive tweets it would post about Joe Biden. The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event.

Research on image sentiment analysis technology based on sparse representation

Many methods based on deep learning have achieved amazing results in image sentiment analysis. However, these existing methods usually pursue high accuracy, ignoring the effect on model training efficiency. Considering that when faced with large-scale sentiment analysis tasks, the high accuracy rate often requires long experimental time. In view of the weakness, a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed, and singular value decomposition (SVD) is used to find the sparse feature of the image, which are sparse vectors with strong discriminativeness and effectively reduce redundant information; The authors propose the Fast Dictionary Learning algorithm (FDL), which can combine neural network with sparse representation. This method is based on K-Singular Value Decomposition, and through iteration, it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy. Moreover, the effectiveness of the proposed method is evaluated on the FER2013 dataset. By adding singular value decomposition, the accuracy of the test suite increased by 0.53%, and the total experiment time was shortened by 8.2%; Fast Dictionary Learning shortened the total experiment time by 36.3%.

Population attitudes toward contraceptive methods over time on a social media platform

Contraceptive method choice is often strongly influenced by the experiences and opinions of one's social network. Although social media, including Twitter, increasingly influences reproductive-age individuals, discussion of contraception in this setting has yet to be characterized. Natural language processing, a type of machine learning in which computers analyze natural language data, enables this analysis. This study aimed to illuminate temporal trends in attitudes toward long- and short-acting reversible contraceptive methods in tweets between 2006 and 2019 and establish social media platforms as alternate data sources for large-scale sentiment analysis on contraception. We studied English-language tweets mentioning reversible prescription contraceptive methods between March 2006 (founding of Twitter) and December 2019. Tweets mentioning contraception were extracted using search terms, including generic or brand names, colloquial names, and abbreviations. We characterized and performed sentiment analysis on tweets. We used Mann-Kendall nonparametric tests to assess temporal trends in the overall number and the number of positive, negative, and neutral tweets referring to each method. The code to reproduce this analysis is available at https://github.com/hms-dbmi/contraceptionOnTwitter. We extracted 838,739 tweets mentioning at least 1 contraceptive method. The annual number of contraception-related tweets increased considerably over the study period. The intrauterine device was the most commonly referenced method (45.9%). Long-acting methods were mentioned more often than short-acting ones (58% vs 42%), and the annual proportion of long-acting reversible contraception-related tweets increased over time. In sentiment analysis of tweets mentioning a single contraceptive method (n=665,064), the greatest proportion of all tweets was negative (65,339 of 160,713 tweets with at least 95% confident sentiment, or 40.66%). Tweets mentioning long-acting methods were nearly twice as likely to be positive compared with tweets mentioning short-acting methods (19.65% vs 10.21%; P<.002). Recognizing the influence of social networks on contraceptive decision making, social media platforms may be useful in the collection and dissemination of information about contraception.

Latent Semantic Scaling: A Semisupervised Text Analysis Technique for New Domains and Languages

ABSTRACT Many social scientists recognize that quantitative text analysis is a useful research methodology, but its application is still concentrated in documents written in European languages, especially English, and few sub-fields of political science, such as comparative politics and legislative studies. This seems to be due to the absence of flexible and cost-efficient methods that can be used to analyze documents in different domains and languages. Aiming to solve this problem, this paper proposes a semisupervised document scaling technique, called Latent Semantic Scaling (LSS), which can locate documents on various pre-defined dimensions. LSS achieves this by combining user-provided seed words and latent semantic analysis (word embedding). The article demonstrates its flexibility and efficiency in large-scale sentiment analysis of New York Times articles on the economy and Asahi Shimbun articles on politics. These examples show that LSS can produce results comparable to that of the Lexicoder Sentiment Dictionary (LSD) in both English and Japanese with only small sets of sentiment seed words. A new heuristic method that assists LSS users to choose a near-optimal number of singular values to obtain word vectors that best capture differences between documents on target dimensions is also presented.

Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

Weakly Supervised Joint Sentiment-Topic Detection from Text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.