Challenges of Sentiment Analysis in Social Networks

Pozzi, Federico; Fersini, Elisabetta; Messina, Enza; Liu, Bing

doi:10.1016/b978-0-12-804412-4.00001-2

Cited by 114 publications

(99 citation statements)

References 4 publications

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“…Data yang berasal dari media sosial dibedakan berdasarkan kategori usergenerated content (i) berbasis profil pengguna seperti Facebook dan MySpace, (ii) microblogging, yang berbasis pada pesan yang dibagikan seperti Twitter, dan (iii) berbasis konten, seperti Youtube, Flickr, dan Instagram (Pozzi, Fersini, Messina, & Liu, 2016). Selain media sosial, mahadata dapat diperoleh dari sensor perangkat digital, CCTV, video, rekam medis, repositori hasil penelitian terdahulu, atau Internet of Things (IoT).…”

Section: Riset Psikologi Dan Mahadata (Big Data)unclassified

“…Revolusi bahasa yang sangat cepat dalam masyarakat kita juga memengaruhi hasil analisis sentimen. Namun tantangan terbesar bersumber pada sifat dari jejaring sosial yang sangat dinamis, heterogen, dan saling terkoneksi (Pozzi et al., 2016 Sebagai gambaran, perhitungan orientasi sentimen diawali dengan (1) perhitungan sentimen dari Tweet atau dokumen lainnya, (2) pemberian label sentimen dari kata sifat yang terdapat pada kamus yang telah dibuat sebelumnya, (3) penghapusan pengguna Tweet kotor dan (4) pembersihan Tweet dari angka, symbol, url, dan sebagainya, (5) pengambilan kata sifat dari kamus pengakar, (6) perhitungan frekuensi total kata sifat pada dokumen, dan (7) penghilangan kata selain kata sifat.…”

Section: Manfaat Dan Keterbatasan Analisisunclassified

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Social Data Analytics sebagai Metode Alternatif dalam Riset Psikologi

2017

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The usage of technological based utilities has been increasing rapidly in our daily life. This phenomenon breeds two fundamental changes: Data explosion and social structures. It takes a different approach to gain insights and benefits from the phenomenon. In psychological science, the acquintance of alternative methods to keep up with the problems should be a necessity. This article introduces two social media data analytical techniques, namely (i) sentiment analysis as the process of computationally identifying, extracting, and quantifying the affective condition towards a particular target, and (ii) social network analysis (SNA) as the process of investigating social structures through the use of Graph Theory dan Network Science. Overviews are presented in general terms with expectation to bring quality of research in Psychology to the next level.

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Section: Riset Psikologi Dan Mahadata (Big Data)unclassified

Section: Manfaat Dan Keterbatasan Analisisunclassified

Social Data Analytics sebagai Metode Alternatif dalam Riset Psikologi

2017

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“…In textual format data, the automatic recognition process plays an important role. Information Extraction and Knowledge Discovery are key research areas focused on data analysis coming from different points of view connected to sources' heterogeneity [25]. For example, the information extraction in web data is complex for their dynamic nature [26], [27].…”

Section: Introductionmentioning

confidence: 99%

“…Among social media, Twitter emerged as the most popular micro-blogging platform, where information proliferates rapidly and posted information and actions cause instantaneous responses from users [42]. Several researchers are discussing how to overcome the limitations of microblog-texts to recognize relevant tweets and extract, annotate and exploit domain-independent, semantic information [25], [43], [44]. Some other researchers are investigating how to exploit machine learning techniques like deep learning [9] in various cases, e.g., in the case of companies that want to know their customers better and opportunely manage the relationships with them, in the case of Arabic sentiment analysis on Twitter [45].…”

Section: Introductionmentioning

confidence: 99%

Analysing and Visualizing Tweets for U.S. President Popularity

Luca

Fallucchi

Giuliano

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In our society we are continually invested by a stream of information (opinions, preferences, comments, etc.). This shows how Twitter users react to news or events that they attend or take part in real time and with interest. In this context it becomes essential to have the appropriate tools in order to be able to analyze and extract data and information hidden in their large number of tweets. Social networks are a source of information with no rivals in terms of amount and variety of information that can be extracted from them. We propose an approach to analyze, with the help of automated tools, comments and opinions taken from social media in a real time environment. We developed a software system in R based on the Bayesian approach for text categorization. We aim of identifying sentiments expressed by the tweets posted on the Twitter social platform. The analysis of sentiment spread on social networks allows to identify the free thoughts, expressed authentically. In particular, we analyze the sentiments related to U.S President popularity by also visualizing tweets on a map. This allows to make an additional analysis of the real time reactions of people by associating the reaction of the single person who posted the tweet to his real time position in Unites States. In particular, we provide a visualization based on the geographical analysis of the sentiments of the users who posted the tweets.

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“…It aggregates the sentiments (document, sentence-or feature-level) of the post. The characteristics of an opinion generate those of the sentiment and consist of reviews, likes or comments [3] [4].…”

Section: The Problem Of Sentiment Analysismentioning

confidence: 99%

Sentiment Detection, Recognition and Aspect Identification

Alasmari¹,

Dahab²

2017

IJCA

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Sentiment analysis is the study of people's opinions, attitudes, feelings, and emotions discuss any object such as entities, events, topics, product, issues, services, etc. are respected for extraction of useful subjective information out of the text. The task is challenging and considered for customers and producers, for instance, Customers need to have general ideas about products, and companies need to know customers' needs and the opportunity to investigate and analyze those opinions towards their products and services. The increasing growth of the social network sites generating a big resource of social data. So, researchers considering about harnessing the effeteness of social media and sentiment analysis. These recent studies classified according to their contributions in the different SA methods. The main target of this paper is to give the illustration of the current trend of research in the sentiment analysis Detection, Recognition, Aspect Identification.

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Challenges of Sentiment Analysis in Social Networks

Cited by 114 publications

References 4 publications

Social Data Analytics sebagai Metode Alternatif dalam Riset Psikologi

Social Data Analytics sebagai Metode Alternatif dalam Riset Psikologi

Analysing and Visualizing Tweets for U.S. President Popularity

Sentiment Detection, Recognition and Aspect Identification

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