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Sentiment analysis is computational research of opinion and emotion that covers textually. This research aims to investigate the Natural Language Processing (NLP) technology in analyzing the sentiment from social media with the Indonesian language. From many methods applied in sentiment analysis, the popular one is Deep Learning (DL). Therefore, this study was conducted to increase sentiment analysis accuracy by knowing the effectiveness of the combination DL algorithm, among others the Convolutional Neural Network (CNN) algorithm and the Long Short Time Memory (LSTM) algorithm. Combining the CNN algorithm and the LSTM algorithm was carried out to process sentiment analysis with tweet data from Twitter. The pre-processing and weighting phase was done using Word2Vec. The result of the experiment with 1295 tweet data showed that the average accuracy of the CNN-LSTM is 71.19% with the highest accuracy of 75.04%. This result indicated that the CNN-LSTM algorithm has the highest accuracy results compared to the basic CNN algorithm and LSTM algorithm. [ABSTRACT FROM AUTHOR]

Sentiment analysis is computational research of opinion and emotion that covers textually. This research aims to investigate the Natural Language Processing (NLP) technology in analyzing the sentiment from social media with the Indonesian language. From many methods applied in sentiment analysis, the popular one is Deep Learning (DL). Therefore, this study was conducted to increase sentiment analysis accuracy by knowing the effectiveness of the combination DL algorithm, among others the Convolutional Neural Network (CNN) algorithm and the Long Short Time Memory (LSTM) algorithm. Combining the CNN algorithm and the LSTM algorithm was carried out to process sentiment analysis with tweet data from Twitter. The pre-processing and weighting phase was done using Word2Vec. The result of the experiment with 1295 tweet data showed that the average accuracy of the CNN-LSTM is 71.19% with the highest accuracy of 75.04%. This result indicated that the CNN-LSTM algorithm has the highest accuracy results compared to the basic CNN algorithm and LSTM algorithm. [ABSTRACT FROM AUTHOR]

Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods. [ABSTRACT FROM AUTHOR]