Sentiment Analysis and Trend Detection in Twitter

Salas-Zárate, María del Pilar; Medina-Moreira, José; Álvarez-Sagubay, Paul Javier; Lagos-Ortíz, Katty; Paredes-Valverde, Mario Andrés; Valencia‐García, Rafael

doi:10.1007/978-3-319-48024-4_6

Cited by 6 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our work, we focused on specified supervised detection approaches. This method was chosen by [9], [25], and [29] for an earthquake, influence and election detection and prediction. While specified unsupervised event detection, was used by [22], he proposed a method for semantic topic extraction and tracking news events and can provide notification and awareness for users.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Social Sensors for Local Services Improvement

Kotevska¹

2017

IJCDS

View full text Add to dashboard Cite

Today, there is an enormous impact on a generation of data in everyday life due to microblogging sites like Twitter, Facebook, and other social networking websites. The valuable data that is broadcast through microblogging can provide useful information to different situations if captured and analyzed properly promptly. In the case of Smart City, automatically identifying event types using Twitter messages as a data source can contribute to situation awareness about the city, and it also brings out much useful information related to it for people who are interested. The focus of this work is an automatic categorization of microblogging data from the certain location, as well as identify the sentiment level at each of the categories to provide a better understanding of public needs and concerns. As the processing of Twitter messages is a challenging task, we propose an algorithm to preprocess the Twitter messages automatically. For the experiment, we used Twitter messages for sixteen different event types from one geolocation. We proposed an algorithm to preprocess the Twitter messages, and Random Forest classifier automatically categorize these tweets into predefined event types. Therefore, applying sentiment analysis to tweets related to these categories allows if people are talking in negative or positive context about it, thus providing valuable information for timely decision making for recommending local service. The results have shown that Random Forest performs better than Support Vector Machines and Naive Bayes classifiers, and combining sentiment score with cosine similarity of event types provides more detailed understanding for the identified public categories.

show abstract

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Social Sensors for Local Services Improvement

Kotevska¹

2017

IJCDS

View full text Add to dashboard Cite

show abstract

“…the number of communities [2]. If the clusters' traits are thoroughly examined, it can aid in identifying the specific character set of individual clusters or groups of like-minded people [3], [4]. To put it another way, the presence of a common connect amongst a group of people implies that those persons share similar ideas and goals.…”

Section: Introductionmentioning

confidence: 99%

Improving Sentiment Analysis in Social Media by Handling Lengthened Words

et al. 2023

View full text Add to dashboard Cite

Machines are continually being channelized in the current era of automation to deliver accurate interpretations of what people communicate on social media. The human species is today engulfed in the concept of what and how people believe, and the decisions made as a result are mostly dependent on the sway of the masses on social media platforms. The usage of internet as well as social media is booming day by day. Today, this ocean of data can be used for the fruitful purposes. Analysis of social media sentiment textual posts can supply knowledge and information that can be used in citizen opinion polling, business intelligence, social contexts, and Internet of Things (IOT)-mood triggered devices. In this manuscript, the main focus is the sentiment analysis based on Emotional Recognition (ER). The proposed system highlights the process of gaining actual sentiment or mood of a person. The key idea to this system is posed by the fact that if smile and laughter can be two different categories of being happy, then why not happpyyyyyy and happy. A novel lexicon based system is proposed that considers the lengthened word as it is, instead of being omitted or normalized. The aggregated intensified senti-scores of lengthened words are calculated using framed lexicon rules. After that, these senti-scores of lengthened words are used to calculate the level of sentiment of the person. The dataset used in this paper is the informal chats happened among different friend groups over Facebook, Tweets and personal chat. The performance of proposed system is compared with traditional systems that ignore lengthened words and proposed system outperform tradition systems by achieving 81% to 96% F-measure rate for all datasets.

show abstract

“…For instance in the consumer industry, user preference for different commodities can be obtained through the analysis of product reviews to help companies adjust sales strategies and make decisions. In social media [2]- [4], sentiment analysis of event comments plays an important role in public opinion control and emergency detection.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Sentiment Analysis Based on Transfer Learning

Liu

Shi

Ji³

et al. 2019

IEEE Access

139

View full text Add to dashboard Cite

With the rapid development of the Internet industry, sentiment analysis has grown into one of the popular areas of natural language processing (NLP). Through it, the implicit emotion in the text can be effectively mined, which can help enterprises or organizations to make an effective decision, and the explosive growth of data undoubtedly brings more opportunities and challenges to the sentiment analysis. At the same time, transfer learning has emerged as a new machine learning technique that uses the existing knowledge to solve different domain problems and produces state-of-the-art prediction results. Many scholars apply transfer learning to the field of the sentiment analysis. This survey summarizes the relevant research results of the sentiment analysis in recent years and focuses on the algorithms and applications of transfer learning in the sentiment analysis, and we look forward to the development trend of the sentiment analysis.INDEX TERMS Sentiment analysis, transfer learning, natural language processing.

show abstract

Sentiment Analysis and Trend Detection in Twitter

Cited by 6 publications

References 26 publications

Sentiment Analysis of Social Sensors for Local Services Improvement

Sentiment Analysis of Social Sensors for Local Services Improvement

Improving Sentiment Analysis in Social Media by Handling Lengthened Words

A Survey of Sentiment Analysis Based on Transfer Learning

Contact Info

Product

Resources

About