Expressing opinions and emotions on social media becomes a frequent activity in daily life. People express their opinions about various targets via social media and they are also interested to know about other opinions on the same target. Automatically identifying the sentiment of these texts and also the strength of the opinions is an enormous help for people and organizations who are willing ...

In the wake of super typhoon Yolanda (known internationally as Haiyan) in the Philippines in 2013, many individuals in the Philippines turned to social media to express their thoughts and emotions in a variety of languages. In order to understand and analyze the sentiment of populations on the ground, we used a novel approach of developing a conceptual Linguistic Inquiry and Word Count (LIWC) d...

本稿では，マクロ経済分野に特化した景気単語極性辞書の構築とその応用例を示す．日本経済新聞の新聞記事コーパスから景気関連の単語の選定し，複数人のエコノミストによるクラスラベルの付与を行った．さらに教師あり学習で収録単語を補完した．また，構築した極性辞書の応用例として，景気動向を迅速に捕捉することが可能な経済ニュース指数を作成した．

In this paper, we propose to build large-scale sentiment lexicon from Twitter with a representation learning approach. We cast sentiment lexicon learning as a phrase-level sentiment classification task. The challenges are developing effective feature representation of phrases and obtaining training data with minor manual annotations for building the sentiment classifier. Specifically, we develo...

The discipline where sentiment/ opinion/ emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found i...

Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention. We propose an efficient method of automatically building the word-emotion mapping dictionary for social emotion detection. In the dictionary, each word is associated with the distribution on a series of human emotions. In addition, three different pruning strategies are proposed...

Cross-Lingual Sentiment Analysis (CLSA) is the task of predicting the polarity of the opinion expressed in a text in a language Ltest using a classifier trained on the corpus of another language Lt rain. Popular approaches use Machine Translation (MT) to convert the test document in Ltest to Lt rain and use the classifier of Lt rain. However, MT systems do not exist for most pairs of languages ...

Short length, multi-targets, target relationship, monetary expressions, and outside reference are characteristics of financial tweets. This paper proposes methods to extract target spans from a tweet and its referencing web page. Total 15 publicly available sentiment dictionaries and one sentiment dictionary constructed from training set, containing sentiment scores in binary or real numbers, a...

The paper contains a description of OPFI: Opinion Finder for the Polish Language (Wawer, 2016), a freely available tool for opinion target extraction. The goal of the tool is opinion finding: a task of identifying tuples composed of sentiment (positive or negative) and its target (about what or whom is the sentiment expressed). OPFI is not dependent on any particular method of sentiment identif...