SentiSAIL: Sentiment Analysis in English, German and Russian

Shalunts, Gayane; Backfried, Gerhard

doi:10.1007/978-3-319-21024-7_6

Cited by 8 publications

(9 citation statements)

References 10 publications

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“…In the context of leveraging the information found online for HADR emergencies, approaches for languages other than English have been limited. Most of which are done by manually constructing resources for a particular language (e.g., in tweets [31]- [33] and in disaster-related news coverage [34]), or by applying cross-language text categorization to build languagespecific models [32], [35].…”

Section: Previous Workmentioning

confidence: 99%

A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

Martínez¹,

Ramakrishna²,

Chiu³

et al. 2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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During the course of a Humanitarian Assistance-Disaster Relief (HADR) crisis, that can happen anywhere in the world, real-time information is often posted online by the people in need of help which, in turn, can be used by different stakeholders involved with management of the crisis. Automated processing of such posts can considerably improve the effectiveness of such efforts; for example, understanding the aggregated emotion from affected populations in specific areas may help inform decisionmakers on how to best allocate resources for an effective disaster response. However, these efforts may be severely limited by the availability of resources for the local language. The ongoing DARPA project Low Resource Languages for Emergent Incidents (LORELEI) aims to further language processing technologies for low resource languages in the context of such a humanitarian crisis. In this work, we describe our submission for the 2019 Sentiment, Emotion and Cognitive state (SEC) pilot task of the LORELEI project. We describe a collection of sentiment analysis systems included in our submission along with the features extracted. Our fielded systems obtained the best results in both English and Spanish language evaluations of the SEC pilot task.

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Section: Previous Workmentioning

confidence: 99%

A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

Martínez¹,

Ramakrishna²,

Chiu³

et al. 2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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“…For training they used 17132 unlabelled reviews, for evaluation they used SentiRuEval's training and testing data with 201 and 203 reviews, respectively. Shalunts and Backfried [28] used a training dataset consisting of 32 news articles in English, 32 in German and 48 in Russian. Testing dataset consists of 50 articles in each language.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…As one can see, to study the sentiment analysis in Latvian, most authors of the articles under survey created their corpora from Twitter [14], [15], [17], [19], [20]. Papers on the sentiment analysis in Russian are more diverse: apart from Twitter [23], authors use news articles [25], [28], [30] and reviews [24], [26], [29]. In the Latvian language, human annotated corpora are small, even if combined together, so researchers are either using noisy-labelling or lexicon-based methods, which do not require a large annotated corpus.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…This has additional importance for languages such as Latvian or Russian, as these languages use inflections, suffixes and prefixes which multiply the number of unique words (and, therefore, potential features) making the problem of sparseness and noise in textual data (see, e.g., [1]) even worse. This step is used in [13], [25], [28]; • Remove transliteration -the Latvian alphabet contains a number of special letters such as "ā", "č", "ū". Instead of these, a user may write double letters: "aa", "ch", "uu".…”

Section: Data Preprocessing and Feature Selectionmentioning

confidence: 99%

“…Some authors also include emoticons or emojis in their feature vectors -either directly [28] or as a separate feature counting them [19], [17]. Shalunts and Backfried [28] used exclamation marks, repeated letters and capitalization as separate sentiment "booster" features that amplify detected sentiment. Gulbinskis [31] extracted specific phrases, which carried information about sentiment in a given text.…”

Section: Data Preprocessing and Feature Selectionmentioning

confidence: 99%

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Sentiment Analysis in Latvian and Russian: A Survey

Vīksna

Jēkabsons

2018

Applied Computer Systems

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Social networking sites such as Facebook, Twitter and VKontakte, online stores such as eBay, Amazon and Alibaba as well as many other websites allow users to share their thoughts with their peers. Often those thoughts contain not only factual information, but also users’ opinion and feelings. This subjective information may be extracted using sentiment analysis methods, which are currently a topic of active research. Most studies are carried out on the basis of texts written in English, while other languages are being less researched. The present survey focuses on research conducted on the sentiment analysis for the Latvian and Russian languages.

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Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach

et al. 2020

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Summary Social media plays a vital role in analyzing the actual emotions of people after and during a disaster. Sentiment analysis is a method to detect a pattern from the emotions and feedback of the user. The main objective of the proposed work is to perform sentiment analysis on the tweets on a specific disaster context for a particular location at different intervals of time. LSTM network with word embedding algorithm is used to derive keywords based on the history of tweets and the context of the tweets. The proposed algorithm risk assessment sentiment analysis (RASA) uses the keywords generated from the network to classify the tweets and sentiment score for each location is identified. The model is validated with various state‐of‐art algorithms, namely, support vector machine, Naive‐Bayes, maximum entropy, logistic regression, random forest, XGBoost, stochastic gradient descent, and convolution neural networks in 2‐fold scenario: one for binary class and the other multiclass with three target classes. The results infer that the proposed RASA performs better in a binary class scenario with an increase of 1% when compared with XGBoost and 30% in multiclass scenario on an average when compared with all the other techniques. The model helps the government to take preventive measures to manage the posteffect of the disaster event in a location.

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SentiSAIL: Sentiment Analysis in English, German and Russian

Cited by 8 publications

References 10 publications

A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

Sentiment Analysis in Latvian and Russian: A Survey

Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach

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