APA 6th Edition Mihaljević, J. (2019). Analys is and Creation of Free Sentiment Analysis Programs. Medijska istraživanja, 25 (1), 83-104. https://doi.org/10.22572/mi.25.1.4
MLA 8th Edition Mihaljević, Josip. "Analys is and Creation of Free Sentiment Analysis Programs." Medijska istraživanja, vol. 25, br. 1, 2019, str. 83-104. https://doi.org/10.22572/mi.25.1.4. Citirano 20.09.2020.
Chicago 17th Edition Mihaljević, Josip. "Analys is and Creation of Free Sentiment Analysis Programs." Medijska istraživanja 25, br. 1 (2019): 83-104. https://doi.org/10.22572/mi.25.1.4
Harvard Mihaljević, J. (2019). 'Analys is and Creation of Free Sentiment Analysis Programs', Medijska istraživanja, 25(1), str. 83-104. https://doi.org/10.22572/mi.25.1.4
Vancouver Mihaljević J. Analys is and Creation of Free Sentiment Analysis Programs. Medijska istraživanja [Internet]. 2019 [pristupljeno 20.09.2020.];25(1):83-104. https://doi.org/10.22572/mi.25.1.4
IEEE J. Mihaljević, "Analys is and Creation of Free Sentiment Analysis Programs", Medijska istraživanja, vol.25, br. 1, str. 83-104, 2019. [Online]. https://doi.org/10.22572/mi.25.1.4
Sažetak This paper analyzes free online programs for sentiment analysis which can, on the bases of their algorithm, give a positive, negative or neutral opinion of a text. At the beginning of the paper sentiment analysis programs and techniques they use such as Naive Bayes and Recurrent Neural Networks are presented. The programs are divided into two categories for analysis. The fi rst category consists of sentiment analysis programs which analyze texts written or copied inside the user interface. The second category consists of programs for analyzing opinions posted on social networks, blogs, and other media sites. Programs from both categories were chosen for this research on the bases of positive reviews on computer science portals and their popularity on web search engin es such as Google and Bing. The accuracy of the programs from the fi rst category was checked by inserting the same sentence from movie reviews and comparing
the results. Their additional options have also been analyzed. For the second category of programs, it was determined which social networks, blogs, and other social media they cover on the internet. The purpose of this analysis was to check the overall quality and options that free sentiment analysis programs provide. An example of how to create one’s own custom sentiment analyzer by using the available Python code and libraries found online is also given. Two simple programs were created using Python. The fi rst program belongs to the fi rst category of programs for analyzing an input text. This program serves as a pilot
program for Croatian which gives only the basic analysis of sentences. The second program collects recent tweets from Twitter containing certain words and creates a pie chart based on the analysis of the results.