Comparing automatically detected reflective texts with human judgements

This paper reports on the descriptive results of an experiment comparing automatically detected reflective and not-reflective texts against human judgements. Based on the theory of reflective writing assessment and their operationalisation five elements of reflection were defined. For each element of reflection a set of indicators was developed, which automatically annotate texts regarding reflection based on the parameterisation with authoritative texts. Using a large blog corpus 149 texts were retrieved, which were either annotated as reflective or notreflective. An online survey was then used to gather human judgements for these texts. These two data sets were used to compare the quality of the reflection detection algorithm with human judgments. The analysis indicates the expected difference between reflective and not reflective texts

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Beyond Sentiment: The Manifold of Human Emotions

Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.Comment: 15 pages, 7 figure

Sentiment analysis tools should take account of the number of exclamation marks!!!

There are various factors that affect the sentiment level expressed in textual comments. Capitalization of letters tends to mark something for attention and repeating of letters tends to strengthen the emotion. Emoticons are used to help visualize facial expressions which can affect understanding of text. In this paper, we show the effect of the number of exclamation marks used, via testing with twelve online sentiment tools. We present opinions gathered from 500 respondents towards “like” and “dislike” values, with a varying number of exclamation marks. Results show that only 20% of the online sentiment tools tested considered the number of exclamation marks in their returned scores. However, results from our human raters show that the more exclamation marks used for positive comments, the more they have higher “like” values than the same comments with fewer exclamations marks. Similarly, adding more exclamation marks for negative comments, results in a higher “dislike”

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

The reader's feeling and text-based emotions : the relationship between subjective self-reports, lexical ratings, and sentiment analysis

In this study, we examined how precisely a sentiment analysis and a word list-based lexical analysis predict the emotional valence (as positive or negative emotional states) of 63 emotional short stories. Both the sentiment analysis and the word list-based analysis predicted subjective valence, which however was predicted even more precisely when both analysis methods were combined. These results can, for example, contribute to the development of new technology-based teaching designs, in that positive or negative emotions in the texts or online-contributions of students can be assessed in automated form and transferred into instructional measures. Such instructional actions can, for example, be hints, learning support or feedback adapted to the students' emotional state