Sentiment analysis on online social network

A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis

Improving microblog retrieval from exterior corpus by automatically constructing a microblogging corpus

A large-scale training corpus consisting of microblogs belonging to a desired category is important for highaccuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and laborconsuming. Therefore, some models for the automatic retrieval of microblogs from an exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.postprin

Characterizing the personality of Twitter users based on their timeline information

Personality is a set of characteristics that differentiate a person from others. It can be identified by the words that people use in conversations or in publications that they do in social networks. Most existing work focuses on personality prediction analyzing English texts. In this study we analyzed publications of the Portuguese users of the social network Twitter. Taking into account the difficulties in sentiment classification that can be caused by the 140 character limit imposed on tweets, we decided to use different features and methods such as the quantity of followers, friends, locations, publication times, etc. to get a more precise picture of a personality. In this paper, we present methods by which the personality of a user can be predicted without any effort from the Twitter users. The personality can be accurately predicted through the publicly available information on Twitter profiles.info:eu-repo/semantics/publishedVersio

Sentiment Analysis Models for Mapping Public Engagement on Twitter Data

Unstructured data in the form of text, which is widely distributed on the internet, often has valuable information. Due to its unstructured form, an effort is needed to extract that information. Twitter is a microblogging social media platform used by many people to express their opinions or thoughts. Sentiment analysis is a way to map a sentence whether the value is positive or not. Sentiment analysis is a series of processes used to classify text documents into two classes, namely positive sentiment class and negative sentiment class. The dataset is obtained from sentiment 140 as training data to build the sentiment analysis model. To test the model, the data used by the crawler algorithm were extracted using the Twitter API. This study focuses on determining public sentiment based on their writing on Twitter. The classification model used in the study is multiclass naive Bayes. The TF-IDF method was also used to weigh the selected feature. The experimental results show that the resulting model has an accuracy of 74.16% with an average precision of 74%, a recall of 74%, and an f-measure of 74%