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"Sentiment": models, code, and papers

SELF & FEIL: Emotion and Intensity Lexicons for Finnish

Apr 28, 2021
Emily Öhman

This paper introduces a Sentiment and Emotion Lexicon for Finnish (SELF) and a Finnish Emotion Intensity Lexicon (FEIL). We describe the lexicon creation process and evaluate the lexicon using some commonly available tools. The lexicon uses annotations projected from the NRC Emotion Lexicon with carefully edited translations. To our knowledge, this is the first comprehensive sentiment and emotion lexicon for Finnish.

* unpublished short paper 

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Machine Learning Evaluation of the Echo-Chamber Effect in Medical Forums

Oct 19, 2020
Marina Sokolova, Victoria Bobicev

We propose the Echo-Chamber Effect assessment of an online forum. Sentiments perceived by the forum readers are at the core of the analysis; a complete message is the unit of the study. We build 14 models and apply those to represent discussions gathered from an online medical forum. We use four multi-class sentiment classification applications and two Machine Learning algorithms to evaluate prowess of the assessment models.

* 17 pages, including Appendix; 6 figures in the main text; 5 tables in the main text and 7 tables in Appendix 

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The Manifold of Human Emotions

Jan 15, 2013
Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan Essa

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.

* 3 pages, 2 figures 

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Revealing the Hidden Patterns of News Photos: Analysis of Millions of News Photos Using GDELT and Deep Learning-based Vision APIs

Mar 24, 2016
Haewoon Kwak, Jisun An

In this work, we analyze more than two million news photos published in January 2016. We demonstrate i) which objects appear the most in news photos; ii) what the sentiments of news photos are; iii) whether the sentiment of news photos is aligned with the tone of the text; iv) how gender is treated; and v) how differently political candidates are portrayed. To our best knowledge, this is the first large-scale study of news photo contents using deep learning-based vision APIs.

* Presented in the first workshop on NEws and publiC Opinion (NECO'16,, colocated with ICWSM'16), Cologne, Germany, 2016 

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Hidden Trends in 90 Years of Harvard Business Review

Oct 20, 2012
Chia-Chi Tsai, Chao-Lin Liu, Wei-Jie Huang, Man-Kwan Shan

In this paper, we demonstrate and discuss results of our mining the abstracts of the publications in Harvard Business Review between 1922 and 2012. Techniques for computing n-grams, collocations, basic sentiment analysis, and named-entity recognition were employed to uncover trends hidden in the abstracts. We present findings about international relationships, sentiment in HBR's abstracts, important international companies, influential technological inventions, renown researchers in management theories, US presidents via chronological analyses.

* 6 pages, 14 figures, Proceedings of 2012 International Conference on Technologies and Applications of Artificial Intelligence 

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Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification

May 04, 2017
Hussam Hamdan

This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4%

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What Can We Learn From Almost a Decade of Food Tweets

Jul 10, 2020
Uga Sproģis, Matīss Rikters

We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking. The corpus has been collected over time-span of over 8 years and includes over 2 million tweets entailed with additional useful data. We also separate two sub-corpora of question and answer tweets and sentiment annotated tweets. We analyse contents of the corpus and demonstrate use-cases for the sub-corpora by training domain-specific question-answering and sentiment-analysis models using data from the corpus.

* In Proceedings of the 9th Conference Human Language Technologies - The Baltic Perspective (Baltic HLT 2020) 

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Analyzing Features for the Detection of Happy Endings in German Novels

Nov 28, 2016
Fotis Jannidis, Isabella Reger, Albin Zehe, Martin Becker, Lena Hettinger, Andreas Hotho

With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.

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MusicMood: Predicting the mood of music from song lyrics using machine learning

Nov 01, 2016
Sebastian Raschka

Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.

* 9 pages, 5 figures 

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Opinion Mining and Analysis: A survey

Jul 12, 2013
Arti Buche, Dr. M. B. Chandak, Akshay Zadgaonkar

The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available in digital form. One important problem in sentiment analysis of product reviews is to produce summary of opinions based on product features. We have surveyed and analyzed in this paper, various techniques that have been developed for the key tasks of opinion mining. We have provided an overall picture of what is involved in developing a software system for opinion mining on the basis of our survey and analysis.

* IJNLC Vol. 2, No.3, June 2013 
* 10 pages 

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