Emotion and sentiment analysis from Twitter text

Sailunaz, Kashfia; Alhajj, Reda

doi:10.1016/j.jocs.2019.05.009

Cited by 287 publications

(149 citation statements)

References 52 publications

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“…Sailunaz and Alhajj [30] (2019) generated their own data set where tweets and replies were analyzed for the sentiments and emotions expressed. The user's influence was further determined using number of followers, retweets, likes etc.…”

Section: Stance and Homophily Detectionmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

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Twitter is one of the most popular social networking platforms of today’s generation and is a fundamental tool in harvesting the data of many users worldwide. Many discussions ranging from current affairs, news sharing, filing complaints to advertising and discussing some common interests etc. happen on Twitter. It is widely used by many famous politicians as a prominent communication medium to address large masses ,owing to its mass usage and popularity . Thus, it is safe to assume that people communicate their political ideologies on Twitter. Many works have been done so far to deduce the stance of user towards a particular party by performing sentiment analysis on their tweets using popular classifiers. To find connected and similar users, earlier works generated a social network graph, based on the assumption that friends and followers share similar interests, which might not be true in all cases. In contrast, the proposed work employs the concept of ensemble classifier (a single classifier generated from several base learning classifiers) to analyze the tweets and makes use of multiple interaction elements like followers/ following, mentions, re-tweets ,hash tags etc. to infer which political party a user identifies with. These interaction elements project out the homophily (users who share same beliefs and choices) amongst the users. The proposed study can be encompassed to any domain and can be used by advertising agencies, marketing companies, e-commerce, heath care etc. to identify their target audience.

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Section: Stance and Homophily Detectionmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…They concluded that some unstable cryptocurrencies might show dependence on Twitter sentiments. Sailunaz and Alhajj [24] created user recommendations for Twitter Users or topics. They showed that analyzing the full text from tweets proved to be better than exploring full text from tweets with only nouns, adjectives, verbs, and adverbs (NAVA).…”

Section: Related Work and Backgroundmentioning

confidence: 99%

A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

Pano

Kashef

2020

BDCC

135

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During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

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“…[4] in the world via social media using local languages. A considerable amount of heterogeneous data is being generated which causes challenges to extract worthy insights, while this information plays a vital role in developing natural language processing (NLP) application, i.e., sentiment analysis [5], risk factor analysis [6], law and order predictor, timeline constructor, opining mining, decision-making systems [7], monitoring social media [8], spam detection, information retrieval, document classification [9], e-mail categorization [10], and sentence classification [11], topic modeling [12], content labeling, and finding the latest trend.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Event Classification from Text

Ali

Missen

Husnain

2021

Scientific Programming

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Social media has become one of the most popular sources of information. People communicate with each other and share their ideas, commenting on global issues and events in a multilingual environment. While social media has been popular for several years, recently, it has given an exponential rise in online data volumes because of the increasing popularity of local languages on the web. This allows researchers of the NLP community to exploit the richness of different languages while overcoming the challenges posed by these languages. Urdu is also one of the most used local languages being used on social media. In this paper, we presented the first-ever event detection approach for Urdu language text. Multiclass event classification is performed by popular deep learning (DL) models, i.e.,Convolution Neural Network (CNN), Recurrence Neural Network (RNN), and Deep Neural Network (DNN). The one-hot-encoding, word embedding, and term-frequency inverse document frequency- (TF-IDF-) based feature vectors are used to evaluate the Deep Learning(DL) models. The dataset that is used for experimental work consists of more than 0.15 million (103965) labeled sentences. DNN classifier has achieved a promising accuracy of 84% in extracting and classifying the events in the Urdu language script.

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Emotion and sentiment analysis from Twitter text

Cited by 287 publications

References 52 publications

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19

Multiclass Event Classification from Text

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