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1 Introduction The purpose of this article is to review various tools available for Polish language sentiment analysis and related areas. The understanding of sentiment analysis in this text is a very broad one: it consists of distinguishing whether a document, sentence, phrase or word is positive, negative or neutral. The field is closely related to opinion mining and opinion classification. It is worth noting that multiple different definitions of sentiment analysis and opinion mining are in use. In his book, Bing Liu ( Liu 2012 ) defines an opinion as a

potential visitors better understand tourist attractions so that they can choose their favorite scenic spots and avoid and reduce trouble throughout the tour. For the reasons mentioned above, there is a need to study Japanese reviews of Chinese attractions to improve the service of tourist attractions. In the scientific communities worldwide, a growing number of studies have focused on sentiment analysis of online reviews. There is a great need for new tools and algorithms which can automatically, efficiently and robustly process the large amounts of user

sentiment interaction is to cultivate learners’ sense of belonging to the community, so that learners are willing to stay in the community for a long time and maintain learning motivation at a high level ( Cho, Kim, & Choi, 2017 ). Academic sentiments are generally hidden in the text records of learning community activities, such as documents, statements, and sentences. Through the techniques of sentiment analysis, weight calculation, and semantic understanding, the sentiment experience related to learning processes can be observed. In this regard, academic sentiment

R eferences [1] K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” Knowledge-Based Systems , vol. 89, pp. 14–46, 2015. https://doi.org/10.1016/j.knosys.2015.06.015 [2] Thomson Reuters, “Thomson Reuters Adds Unique Twitter and News Sentiment Analysis to Thomson Reuters Eikon” [Online]. Available: https://www.thomsonreuters.com/en/press-releases/2014/thomson-reuters-adds-unique-twitter-and-news-sentiment-analysisto-thomson-reuters-eikon.html . [Accessed: Mar.8, 2018]. [3] L. Chen, G. Chen, and F. Wang

1 Introduction Platforms such as social networks, micro-blogs, online reviews, and discussion forums are growing very fast, and thus, the need for analyzing the sentiments of the users are also increasing. Sentiment analysis proves to be very effective in businesses and social domains because opinions matter and is critical for all human activities, and thus, they have become the key influence of human behavior. There are two broadly categorized types of the textual representation: one is fact; another is opinion. While facts are objective expressions

. 1631-1642. [3] High, R. (2012), The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works , Redbooks and IBM Corporation, vol. 1. [4] Ferrucci, D. (2012), Introduction to “This is Watson” , IBM Journal of Research and Development, vol. 56, pp. 1-15. [5] Kushwanth, R., Sachin, A., Shambhavi, B. and Shobha, G. (2014), Sentiment Analysis of Twitter Data , International Journal of Advanced Research in Computer Engineering and Technology (IJARCET 2014), vol. 3, no. 12, pp. 4337-4342. [6] Pak, A. and Paroubek, P. (2010), Twitter as a Corpus for

1 Introduction User-generated opinionated data are increasing day by day through sources such as blogs, online forums, social media, and microblogging websites. Such data contain opinions about any product, topic, service, or any idea and, thus, can be effectively used for extracting valuable information from them. Among the various sources of opinionated data, Twitter is a gold mine and rich source as people tweet on each and every topic. Twitter sentiment analysis is one of the techniques for determining aggregated feelings of people from opinionated

event [ 10 ], [ 58 ]. The information retrieved from micro-blogs may involve at least two specific issues: firstly, use of formal languages, all in electronic word-of-mouth, which may lead to misspellings and use of slang words. Secondly, the limited characters which may tend to shortened words or sentences making analysis difficult. The detection and analysis of sentiments in short texts is an attractive topic, for many researchers and practitioners, to classify text into different polarities or classes. A sentiment analysis is a process of automatically extracting

they reflect, is of great scope. According to Google in collaboration with KPMG ( https://assets.kpmg.com/content/dam/kpmg/in/pdf/2017/04/Indian-languages-Defining-Indias-Internet.pdf ), most of the people in India prefer tweeting in their regional languages, and hence, sentiment analysis of data in Indian languages has become significant. Sentiment analysis uses statistics, natural language processing (NLP) and machine learning techniques to predict the polarity of a sentence and gauge the correctness of the sentiment deduced. There has been much research on English

classifications are still the dominant approach in sentiment analysis ( Nissim and Patti 2017 : 32–34; Pozzi et al. 2017 : 3) and do allow researchers to get a first glimpse at the emotional language of a speaker. Yet, they obviously fail to provide a detailed analysis of the complex set of emotions that speakers have. Sadness and fear, for example, are both negative emotions, yet, as we all know, they crucially differ in their physiological as well as psychological effects. Similarly, we consider joy and trust as positive emotions, but are well aware of how different we feel