Sentiment classification of twitter data belonging to renewable energy using machine learning

Jain, Achin; Jain, Vanita

doi:10.1080/02522667.2019.1582873

Cited by 27 publications

(13 citation statements)

References 14 publications

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“…Researchers are beginning to use social media, especially Twitter, to examine public sentiment toward renewable energy [l , 23 , 46, 37]. Jain and Jain [23] compares five different machine learning technique s for sentiment analysis and finds that the Support Vector Machine (SVM) achieves higher accuracy than K-Nearest Neighbor, Naive Bayes, AdaBoost, and Bagging algorithms for sentiment classification on renewable energy related tweet s. Using both traditional and social media for opinion mining , Nuortimo and Harkonen [46 ] find that public opinion on solar and wind has been the most positive compared to other energy sources, including coal, nuclear , and bioma ss. Using Twitter data from 2014 to 2016, Abdar et al [1] finds that Alaskans' energy preferences have become more supportive of renewable energy over time.…”

Section: Introductionmentioning

confidence: 99%

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

et al. 2021

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Public acceptance and support for renewable energy are important determinants of the low-carbon energy transition. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform on which people post messages, known as tweets. We filtered tweets specific to solar energy and performed a classification task using Robustly optimized Bidirectional Encoder Representations from Transformers (RoBERTa). Our RoBERTa-based sentiment classification model, fine-tuned with 6300 manually annotated tweets specific to solar energy, attains 80.2% accuracy for ternary (positive, neutral, or negative) classification. Analyzing 266,686 tweets during the period of January to December 2020, we find public sentiment varies widely across states (Coefficient of Variation =164.66%). Within the study period, the Northeast U.S. region shows more positive sentiment toward solar energy than did the South U.S. region. Public opinion on solar energy is more positive in states with a larger share of Democratic voters in the 2020 presidential election. Public sentiment toward solar energy is more positive in states with consumer-friendly net metering policies and a more mature solar market. States that wish to gain public support for solar energy might want to consider implementing consumer-friendly net metering policies and support the growth of solar businesses.

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Section: Introductionmentioning

confidence: 99%

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

et al. 2021

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“…The operation minimum is the most operation used in the implication of Mamdani operation. The following equation (34) describes this implication phase :…”

Section: Figure 20 Representation Graphic Of Trapezoidal Functionmentioning

confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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“…Jain et al performed classification and sentiment analysis of the tweets containing the hashtag '#RenewableEnergy'. To classify the tweets, the five types of machine learning (K Nearest Neighbor, Support Vector Machine, Naïve Bayes, Adaboost, and Bagging) were applied, and the support vector machine was found to be with the highest accuracy [15].…”

Section: Ntanos Et Al Conducted a Survey To Understand The Greek Peop...mentioning

confidence: 99%

Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy

et al. 2021

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To achieve carbon neutrality by 2050, Korea has been expanding its investment in renewal energy distribution and technology development. However, with this rapid expansion of renewable energy, public concern about it has grown. This study developed and used a big data analysis-based procedure to analyze the questions registered on Naver, the largest portal site in Korea, from 2008 to 2020 to identify public concern over renewable energy. The big data analysis-based procedure consisted of two steps. The first was a frequency analysis to identify the most frequently registered words. The second was to classify questions using term frequency-inverse document frequency (TF-IDF) weight and cosine similarity based on word2vec. The analysis revealed the most frequently registered words related to renewable energy, such as “solar power,” “power generation,” “energy,” and “wind power.” It also revealed the most frequently registered questions, such as those related to solar panel installation, renewable energy generation methods, and certificates. To continue expanding renewable energy, it is becoming increasingly important to understand the public’s concerns and create a method to resolve their objections to renewable energy. It is expected that the procedure in this study may provide relevant insight for the method.

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Sentiment classification of twitter data belonging to renewable energy using machine learning

Cited by 27 publications

References 14 publications

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

Public Sentiment toward Solar Energy—Opinion Mining of Twitter Using a Transformer-Based Language Model

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy

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