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"Sentiment": models, code, and papers

Extracting Sentiment Attitudes From Analytical Texts

Aug 27, 2018
Natalia Loukachevitch, Nicolay Rusnachenko

In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations. For each document we annotated sentiments from the author to mentioned named entities, and sentiments of relations between mentioned entities. In the current experiments, we considered the problem of extracting sentiment relations between entities for the whole documents as a three-class machine learning task. We experimented with conventional machine-learning methods (Naive Bayes, SVM, Random Forest).

* Proceedings of International Conference on Computational Linguistics and Intellectual Technologies Dialoque-2018, 2018, p.459-468 

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Basic tasks of sentiment analysis

Oct 18, 2017
Iti Chaturvedi, Soujanya Poria, Erik Cambria

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about.

* Encyclopedia of Social Network Analysis and Mining, 2017 

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Sentiment Recognition in Egocentric Photostreams

Mar 29, 2017
Estefania Talavera, Nicola Strisciuglio, Nicolai Petkov, Petia Radeva

Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral).


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Author's Sentiment Prediction

Nov 12, 2020
Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, Niranjan Balasubramanian

We introduce PerSenT, a dataset of crowd-sourced annotations of the sentiment expressed by the authors towards the main entities in news articles. The dataset also includes paragraph-level sentiment annotations to provide more fine-grained supervision for the task. Our benchmarks of multiple strong baselines show that this is a difficult classification task. The results also suggest that simply fine-tuning document-level representations from BERT isn't adequate for this task. Making paragraph-level decisions and aggregating them over the entire document is also ineffective. We present empirical and qualitative analyses that illustrate the specific challenges posed by this dataset. We release this dataset with 5.3k documents and 38k paragraphs covering 3.2k unique entities as a challenge in entity sentiment analysis.

* 12 pages, 5 figures, Accepted in COLING2020 

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Sentiment Index of the Russian Speaking Facebook

Aug 23, 2018
Alexander Panchenko

A sentiment index measures the average emotional level in a corpus. We introduce four such indexes and use them to gauge average "positiveness" of a population during some period based on posts in a social network. This article for the first time presents a text-, rather than word-based sentiment index. Furthermore, this study presents the first large-scale study of the sentiment index of the Russian-speaking Facebook. Our results are consistent with the prior experiments for the English language.

* In Proceedings of the 20th International Conference on Computational Linguistics and Intellectual Technologies (Dialogue'2014). Moscow, Russia. RGGU 

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Sentiment Classification using Images and Label Embeddings

Dec 03, 2017
Laura Graesser, Abhinav Gupta, Lakshay Sharma, Evelina Bakhturina

In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, and models which combined both data types. We also analysed if this approach could help sentiment classifiers generalize to unknown sentiments.

* 13 pages, 3 figures, 9 tables. Technical report for Statistical Natural Language Processing Project (NYU CS - Fall 2016) 

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Benchmarking Multimodal Sentiment Analysis

Jul 29, 2017
Erik Cambria, Devamanyu Hazarika, Soujanya Poria, Amir Hussain, R. B. V. Subramaanyam

We propose a framework for multimodal sentiment analysis and emotion recognition using convolutional neural network-based feature extraction from text and visual modalities. We obtain a performance improvement of 10% over the state of the art by combining visual, text and audio features. We also discuss some major issues frequently ignored in multimodal sentiment analysis research: the role of speaker-independent models, importance of the modalities and generalizability. The paper thus serve as a new benchmark for further research in multimodal sentiment analysis and also demonstrates the different facets of analysis to be considered while performing such tasks.

* Accepted in CICLing 2017 

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Improving Results on Russian Sentiment Datasets

Jul 28, 2020
Anton Golubev, Natalia Loukachevitch

In this study, we test standard neural network architectures (CNN, LSTM, BiLSTM) and recently appeared BERT architectures on previous Russian sentiment evaluation datasets. We compare two variants of Russian BERT and show that for all sentiment tasks in this study the conversational variant of Russian BERT performs better. The best results were achieved by BERT-NLI model, which treats sentiment classification tasks as a natural language inference task. On one of the datasets, this model practically achieves the human level.

* 13 pages, 8 tables. Accepted to AINL-2020 conference (https://ainlconf.ru/

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Stance and Sentiment in Tweets

May 05, 2016
Saif M. Mohammad, Parinaz Sobhani, Svetlana Kiritchenko

We can often detect from a person's utterances whether he/she is in favor of or against a given target entity -- their stance towards the target. However, a person may express the same stance towards a target by using negative or positive language. Here for the first time we present a dataset of tweet--target pairs annotated for both stance and sentiment. The targets may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. Partitions of this dataset were used as training and test sets in a SemEval-2016 shared task competition. We propose a simple stance detection system that outperforms submissions from all 19 teams that participated in the shared task. Additionally, access to both stance and sentiment annotations allows us to explore several research questions. We show that while knowing the sentiment expressed by a tweet is beneficial for stance classification, it alone is not sufficient. Finally, we use additional unlabeled data through distant supervision techniques and word embeddings to further improve stance classification.

* 22 pages 

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Beyond Sentiment: The Manifold of Human Emotions

Aug 08, 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. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.

* Proceedings of the 16 International Conference on Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, USA. Volume 31 of JMLR: W&CP 31 
* 15 pages, 7 figures 

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