Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.Comment: 9 pages, 5 figures, AAAI 201

A large-scale sentiment analysis using political tweets

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

Antecedents of retweeting in a (political) marketing context

Word of mouth disseminates across Twitter by means of retweeting; however the antecedents of retweeting have not received much attention. This study uses the CHAID decision tree predictive method (Kass, 1980) with readily available Twitter data, and manually coded sentiment and content data, to identify why some tweets are more likely to be retweeted than others in a (political) marketing context. The analysis includes four CHAID models: (i) using message structure variables only, (ii) source variables only, (iii) message content and sentiment variables only and (iv) a combined model using source, message structure, message content and sentiment variables. The aggregated predictive model correctly classified retweeting behavior with a 76.7% success rate. Retweeting tends to occur when the originator has a high number of Twitter followers and the sentiment of the tweet is negative, contradicting previous research (East, Hammond, & Wright, 2007; Wu, 2013) but concurring with others (Hennig-Thurau, Wiertz, & Feldhaus, 2014). Additionally, particular types of tweet content are associated with high levels of retweeting, in particular those tweets including fear appeals or expressing support for others, whilst others are associated with very low levels of retweeting, such as those mentioning the sender’s personal life. Managerial implications and research directions are presented. The study makes a methodological contribution by illustrating how CHAID predictive modelling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context

Hashtag Politics: A Twitter Sentiment Analysis of the 2015 Canadian Federal Election

We developed a split plot design model for analysis of sentiment toward federal political parties on the social media platform Twitter in the weeks prior to the 2015 Canadian Federal Election. Data was collected from Twitter’s Application Programming Interface (API) via statistical program R. We scored the sentiment of each Twitter message referring to the parties and tested using ANOVA. Our results suggested that the Liberal Party and New Democratic Party had more positive sentiment than the Conservative Party. Actual seat wins coincide with our results for the Liberal Party (which won 148 new seats) and the Conservative Party (which lost 60 seats), but positive sentiment for the New Democratic Party did not correspond to seat wins

Opini Masyarakat Twitter terhadap Kandidat Bakal Calon Presiden Republik Indonesia Tahun 2024

Registration for the 2024 presidential candidates began at the end of 2023, but the euphoria of the supporters of the 2024 presidential candidates began to be felt from the beginning of 2022. Several survey institutions released public opinions regarding several prospective 2024 presidential candidates. One of the approaches taken in the survey was by conducting direct interviews with the public. However, political dynamics can change the results of political surveys at great expense. Public opinion about the 2024 presidential candidates cannot only be acquired through direct interviews. Public opinion acquisition can also be done through social media such as Twitter. This article aims to find out public opinion on the candidates for the 2024 presidential candidate on Twitter social media. This article uses a Twitter dataset and data analysis tools using orange data mining. The crawling dataset was carried out using the hashtags #capres2024 and #presiden2024 and the keywords anies baswedan, prabowo subianto and ganjar pranowo with 10,000 tweet data in content written in Indonesian. Text preprocessing includes transformation, tokenization, filtering and normalization applied to data before analysis is carried out with topic modeling and sentiment towards the presidential candidates. The results of the word cloud analysis show a very high level of popularity for candidate Ganjar Pranowo, but the results of the sentiment analysis show that Ganjar Pranowo has a negative sentiment

Sentiment Analysis of 2024 Presidential Candidates Election Using SVM Algorithm

Elections for presidential candidates are held every 5 years with various candidates, especially on Twitter, arguments about political matters often occur that many Twitter users participate in discussions about the election for presidential candidate. Therefore, this study focuses on sentiment analysis to infer user responses to the presidential election and validate it by looking for a correlation between electability survey results and Twitter sentiment data using Pearson Correlation. In sentiment analysis model, the 10-Fold Cross Validation method is used to find the best model from a dataset with a division of training data and test data with 90:10 split. Then the alphabetic data will be converted into numeric data using the TF-IDF weighting method. To validate the best model, Confusion Matrix is used to get the best f1-score. The model is using Support vector machine algorithm with the Gaussian RBF (Radial Basis Function) kernel. The results of the analysis are compared with the results of the news portal electability survey which contains the 3 candidates using Pearson Correlation. This study produces the best fold for each data on each presidential candidate with the f1-score to find the best model for each fold. In the Peason Correlation result, the higher positive sentiment of each presidential candidate, the higher electability survey data. For further research, research can be discuss about hyper tuning parameters and using other kernels on Support vector machine algorithm

Opinion Mining Using Twitter Feeds for Political Analysis

Sentiment analysis deals with identifying and understanding opinions and sentiments expressed in a particular text. The masses give their opinion regarding various subjects on social media platforms using tweets, status updates and blogs. By analyzing this very data, we can gain better insight of the public opinion on any subject in specific. On performing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters allowed in Twitter is 140. In this paper, we try to analyze the twitter posts about government issues and political reforms. The proposed framework uses Twitter as the platform to analyze the emotions of the users using Sentiment Analysis. The system will use the opinions of the users, analyze the reaction and then map it to the appropriate region

Sentiment Analysis of Political Tweets From the 2019 Spanish Elections

The use of sentiment analysis methods has increased in recent years across a wide range of disciplines. Despite the potential impact of the development of opinions during political elections, few studies have focused on the analysis of sentiment dynamics and their characterization from statistical and mathematical perspectives. In this paper, we apply a set of basic methods to analyze the statistical and temporal dynamics of sentiment analysis on political campaigns and assess their scope and limitations. To this end, we gathered thousands of Twitter messages mentioning political parties and their leaders posted several weeks before and after the 2019 Spanish presidential election. We then followed a twofold analysis strategy: (1) statistical characterization using indices derived from well-known temporal and information metrics and methods –including entropy, mutual information, and the Compounded Aggregated Positivity Index– allowing the estimation of changes in the density function of sentiment data; and (2) feature extraction from nonlinear intrinsic patterns in terms of manifold learning using autoencoders and stochastic embeddings. The results show that both the indices and the manifold features provide an informative characterization of the sentiment dynamics throughout the election period. We found measurable variations in sentiment behavior and polarity across the political parties and their leaders and observed different dynamics depending on the parties’ positions on the political spectrum, their presence at the regional or national levels, and their nationalist or globalist aspirations

Sentiment Analysis of Twitter Data

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, emotions, political and religious views from written language about personality, product or event and determined whether they are viewed positively or negatively. Our project will involve collection of data from web resources such as twitter by using Hadoop and intend to derive useful inferences and recommendations. From the evaluation of this study it can be concluded that the proposed machine learning and natural language processing techniques are an effective and practical methods for sentiment analysis

Sentiment Analysis of Portuguese Political Parties Communication

Costa, C. J., Aparicio, M., & Aparicio, J. (2021). Sentiment Analysis of Portuguese Political Parties Communication. In Proceedings of the 39th ACM International Conference on the Design of Communication (SIGDOC '21) (pp. 63-69). Association for Computing Machinery (ACM). https://doi.org/10.1145/3472714.3473624Political communication in social media has gained increasing importance in the last years. In this study, we analyze the political parties’ communication on Twitter and understand the sentiment of their communication. First by identifying their communication performance regarding the daily number of tweets, favorite tweets, number of retweets per day and per political party. We present a sentiment analysis by the political party using tweets data. In this study, we propose an explanatory model with the main drivers of retweets. To conduct this study, our approach used data analysis and machine learning techniques methods. Results indicate the main determinants that influence future retweets of political posts globally. Here we present a comparison of the communication content between tweets posts and the political parties’ programs available on their institutional websites. We identify the similarities between tweets and formal programs per party and among all parties. This study contributes to analyze the coherence and effectiveness of the political parties’ communication.authorsversionpublishe