Reliability and Perceived Value of Sentiment Analysis for Twitter Data

Jussila, Jari; Vuori, Vilma; Okkonen, Jussi; Helander, Nina

doi:10.1007/978-3-319-56288-9_7

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Various research studies have used twitter as an information source to investigate the consumer's opinion regarding brands [16]; the social media platform twitter is a rapid and useful mode for the company to find out, that how consumers feel regarding their business and managers [17]. Emotion is the positive expression or negative feelings of a consumer by social media with a definitive purpose [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

et al. 2021

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Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing.

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Section: Literature Reviewmentioning

confidence: 99%

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

et al. 2021

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“…The best binary classifiers were found to be Support Vector Machine [21], Naïve Bayes [22] and Logistic Regression [54]. To measure the model performance on our dataset, we computed the accuracy, precision, recall, F1 score and area under the curve (AUC) scores.…”

Section: Feature Extraction and Classification Modelsmentioning

confidence: 99%

“…It has become a notable vehicle for the fast creation, transmission and discourse of news stories [18][19][20]. Entirely due to the help from Twitter, Web 2.0 has evolved into a much bigger platform for marketing campaigns [21,22], research-work discussion and sentiment analysis [23][24][25][26]. Twitter is used by many research communities as a channel to increase visibility and reach broader audiences that are interested in discussing scientific literature [27,28], and the most Altmetrics Social Media Metrics research has either focused on or included Twitter [4].…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of tweets through Altmetrics: A machine learning approach

Hassan

Saleem

Soroya

et al. 2020

Journal of Information Science

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The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.

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“…An alternate method is to compare the agreement between two different sentiment lexicons. 45 We utilized this approach and the comparison lexicon used here was the R-Studio package "bing." The percent agreement using this approach was 92.3.…”

Section: Outcome Measurementmentioning

confidence: 99%

Quantifying Experiences with Telepractice for Aphasia Therapy: A Text Mining Analysis of Client Response Data

2020

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Measures of satisfaction following treatment for aphasia have been limited. The challenge associated with reduced verbal output among many persons with aphasia (PWA) has reportedly been a key reason measures of treatment satisfaction have been limited. A novel approach to measure treatment satisfaction is the use of content analysis (CA), which uses the presence of certain words, themes, or concepts to explore outcomes such as treatment satisfaction particularly among individuals who generate limited output. CA utilizes responses and response patterns to assign meaning to client responses. The aim of this study was to use CA to measure posttreatment satisfaction with a telepractice approach for aphasia treatment. Seventeen PWA received 12 treatment sessions over a 6-week period. At the conclusion of the treatment, CA was utilized to explore patient satisfaction with this treatment approach. The participants reported an overall positive sentiment for the telepractice approach. Two primary topics emerged which were healthcare provider and healthcare delivery, where text analysis revealed discussion of these topics to be centered around being “helpful” and “being effective.” This study demonstrated that CA can be an effective approach for determining satisfaction with aphasia treatment particularly among PWA with limited verbal abilities.

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Reliability and Perceived Value of Sentiment Analysis for Twitter Data

Cited by 17 publications

References 12 publications

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics

Sentiment analysis of tweets through Altmetrics: A machine learning approach

Quantifying Experiences with Telepractice for Aphasia Therapy: A Text Mining Analysis of Client Response Data

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