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Abstract
In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.
Highlights
In this advancing world of technology, expressing emotions, feelings, and views regarding any and every situation is much easier through social networking sites
Accuracy, area under the receiver operating characteristics (ROC) curve, recall, false positive rate, precision, and F-measure were used to analyze the results of the classification algorithms naïve Bayes, support vector machine (SVM), random forest, and K-nearest neighbor
(positive sentiment), TPB be the true positives of the class B, and TPC be the number of true positives of class C
Summary
In this advancing world of technology, expressing emotions, feelings, and views regarding any and every situation is much easier through social networking sites. Sentiment analysis along with opinion mining are two processes that aid in classifying and investigating the behavior and approach of customers in regards to the brand, product, events, company, and customer services [1]. There are many approaches used for sentiment analysis on linguistic data, and the approach to be used depends on the nature of the data and the platform. Machine learning techniques control the data processing by the use of machine learning algorithms and by classifying the linguistic data by representing them in vector form [4]. A lexicon-based ( called dictionary-based) approach classifies the linguistic data using a dictionary lookup database. During this classification, IoT 2020, 1, 218–239; doi:10.3390/iot1020014 www.mdpi.com/journal/iot
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