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Sentiment analysis is used to extract people’s opinion from their online comments in order to help automated systems provide more precise recommendations. Existing sentiment analysis methods often assume that the comments of any single reviewer are independent of each other and so they do not take advantage of significant information that may be extracted from reviewers’ comment histories. Using psychological findings and the theory of negativity bias, we propose a method for exploiting reviewers’ comment histories to improve sentiment analysis. Furthermore, to use more fine-grained information about the content of a review, our method predicts the overall ratings by aggregating sentence-level scores. In the proposed system, the Dempster–Shafer theory of evidence is utilized for score aggregation. The results from four large and diverse social Web datasets establish the superiority of our approach in comparison with the state-of-the-art machine learning techniques. In addition, the results show that the suggested method is robust to the size of training dataset.

Sentiment prediction techniques are often used to assign numerical scores to free-text format reviews written by people in online review websites. In order to exploit the fine-grained structural information of textual content, a review may be considered as a collection of sentences, each with its own sentiment orientation and score. In this manner, a score aggregation method is needed to combine sentence-level scores into an overall review rating. While recent work has concentrated on designing effective sentence-level prediction methods, there remains the problem of finding efficient algorithms for score aggregation. In this study, we investigate different aggregation methods, as well as the cases in which they perform poorly. According to the analysis of existing methods, we propose a new score aggregation method based on theDempster-Shafer theory of evidence. In the proposed method, we first detect the polarity of reviews using a machine learning approach and then, consider sentence scores as evidence for the overall review rating. The results from two public social web datasets show the higher performance of our method in comparison with existing score aggregation methods and state-of-the-art machine learning approaches.

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There are repetitive patterns in strategies of manipulating source code. For example, modifying source code before acquiring knowledge of how a code works is a depth-first style whereas reading and understanding before modifying source code is a breadth-first style. To the extent we know, there is no study on the influence of personality on them. The objective of this study is to understand the influence of personality on programming styles. We did a correlational study with 65 programmers at the University of Stuttgart. Academic achievement, programming experience, attitude towards programming, and five personality factors were measured via self-assessed survey. The programming styles were asked for in the survey or mined from the software repositories. Performance in programming was composed of bug-proneness of programmers which was mined from software repositories, the grades they got in a software project course and their estimate of their own programming ability. In a statistical analysis we found that Openness to Experience has a positive association with breadth-first style and Conscientiousness has a positive association with depth-first style. We also found that, in addition to having more programming experience and better academic achievement, the styles of working depth-first and saving coarse-grained revisions improve performance in programming.