This study examines the effect of investor sentiment on the accruals anomaly. We find that for small stocks mispricing per unit of accruals is greater in high sentiment periods as compared with low sentiment periods. This result is consistent with the notion that in high sentiment periods individual investors pay less attention toward understanding the accruals and cash flow components of earni...

Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application’s rich core lexicons, and employs context rules to accou...

The rapid accumulation of data in social media (in million and billion scales) has imposed great challenges in information extraction, knowledge discovery, and data mining, and texts bearing sentiment and opinions are one of the major categories of user generated data in social media. Sentiment analysis is the main technology to quickly capture what people think from these text data, and is a r...

Sentiment analysis of Twitter data is performed. The researcher has made the following contributions via this paper: (1) an innovative method for deriving sentiment score dictionaries using an existing sentiment dictionary as seed words is explored, and (2) an analysis of clustered tweet sentiment scores based on tweet length is performed.

Although sentiment analysis in Chinese social media has attracted a lot of interest in recent years, it has been less explored in traditional Chinese literature (e.g., classical Chinese poetry) due to the lack of sentiment lexicon resources. In this paper, we propose a weakly supervised approach based on Weighted Personalized PageRank (WPPR) to create a sentiment lexicon for classical Chinese p...

Sentiment lexicons are the most used tool to automatically predict sentiment in text. To the best of our knowledge, there exist no openly available sentiment lexicons for the Norwegian language. Thus in this paper we applied two different strategies to automatically generate sentiment lexicons for the Norwegian language. The first strategy used machine translation to translate an English sentim...

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phr...

As companies and organizations increasingly rely on on-line, user-supplied data to obtain valuable insights into their operations, sentiment analysis of textual data has proven to be a most valuable resource. To understand how sentiment analysis can be used effectively, it is important to identify what types of sentiment analysis could be employed during the analysis of a given situation. This ...

Sentiment Analysis aims to determine the overall sentiment orientation of a given input text. One motivation for research in this area is the need for consumer related industries to extract public opinion from online portals such as blogs, discussion boards, and reviews. Estimating sentiment orientation in text involves extraction of sentiment rich phrases and the aggregation of their sentiment...

Tweet sentiment analysis has been an effective and valuable technique in the sentiment analysis domain. As the most widely used approach for tweet sentiment analysis, machine learning algorithms work well on the sentiment classification, just as they have been successfully applied for many other purposes. In this thesis, we conduct a systematic and thorough empirical study on the machine learni...