We can leverage data and complex systems science to better understand society and human nature on a population scale through language — utilizing tools that include sentiment analysis, machine learning, and data visualization. Data-driven science and the sociotechnical systems that we use every day are enabling a transformation from hypothesis-driven, reductionist methodology to complex systems...

Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public trending issues like “yahoo-yahoo” which in Nigeria, is synonymous cybercrime. In this study, content analysis of selected historical tweets hash-tag was conducted for sentiment and topic modelling. A corpus 5500 obtained pre-processed using a pre-trained tweet tokenizer while Valence ...

This paper takes the application of international Chinese to foreigners on Internet as research object. A variety features are constructed according characteristics and foreign texts networks. selects three features: dictionary-based sentiment value feature, expression improved semantic feature. text classification model is formed by fusing multiple features. Compared with traditional other sin...

The present study aimed to examine the effect of three vocabulary techniques (dictionary use, etymological analysis, and glossing) on the Iranian ESP learners' vocabulary production. Forty-five university students majoring in architecture at Azad University, Anzali branch,  participated in this study. They were divided into three groups, and each group was randomly assigned to one kind of treat...

For sentiment analysis, lexicons play an important role in many related tasks. In this paper, aiming to build Chinese emotion lexicons for public use, we adopted a graph-based algorithm which ranks words according to a few seed emotion words. The ranking algorithm exploits the similarity between words, and uses multiple similarity metrics which can be derived from dictionaries, unlabeled corpor...

We present two supervised sentiment detection systems which were used to compete in SemEval-2014 Task 9: Sentiment Analysis in Twitter. The first system (Rosenthal and McKeown, 2013) classifies the polarity of subjective phrases as positive, negative, or neutral. It is tailored towards online genres, specifically Twitter, through the inclusion of dictionaries developed to capture vocabulary use...

Sentiment analysis and affect detection algorithms are generally based onto annotated data, structured into dictionaries, ontologies or word nets. The focus, so far, has been concentrated on manual annotation of the data, and then, in some situations, a semantic valence propagation is applied. The problem with this approach is that while it is able to build new affective labels through the prop...

In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) ...

Sentiment analysis relies to a large extent on lexical resources. While lists of words bearing a contextindependent evaluative polarity (‘great’, ‘bad’) are available for many languages now, the automatic extraction of domain-specific evaluative vocabulary still needs attention. This holds especially for implicit opinions or so-called polar facts. In our work, we focus on German and on a genre ...