Topics in the Russian Twitter and Relations between their Interpretability and Sentiment

Bodrunova, Svetlana S.; Blekanov, Ivan S.; Kukarkin, Mikhail

doi:10.1109/snams.2019.8931725

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…We have also applied BTM to detect the dynamics of topicality in conflictual discussions (Smoliarova et al 2018) and have demonstrated that the saliency of topics in time may help detect pivotal points in mediated discussions. Experiments with datasets on Twitter in three languages, including Russian (Smoliarova et al 2018, Bodrunova et al 2019a, show that sentiment of tweets is linked to topicality: thus, more interpretable topics are more sentiment-loaded, in particular negativityloaded (Bodrunova et al 2019a). Another study (Bodrunova et al 2019b) has shown that topic interpretability may be linked to topic robustness and topic saliency.…”

Section: Topic Modeling For the Russian Twittermentioning

confidence: 99%

“…The author shows that normalized PMI (NPMI) suggested in the paper outperforms PMI as well as other conventional metrics like tf-idf, but vector-based metrics work even better than NPMI. But the question remains whether both NPMI and word2vec metrics work well for short texts, as there is evidence that NPMI marks the topics as good while they remain low-interpretable for human coders (Bodrunova et al 2019b). For automated topic assessment versus human interpretability, an important attempt to introduce a quality metric has recently been made.…”

Section: Quality Assessment and Interpretability Of The Russian-langumentioning

confidence: 99%

“…In our pilot studies, though, we have seen that topics for Twitter are less interpretable, with only up to 40-45% identified as such in all the three languages (Bodrunova et al 2019a, b); thus, it is not the nature of Russian alone that seems to be causing lower topic interpretability in the case of Russian Twitter. Also, we examined the features of top words and found that their negative sentiment could actually raise topic interpretability (Bodrunova et al 2019a).…”

Section: Quality Assessment and Interpretability Of The Russian-langumentioning

confidence: 99%

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Topic Modeling in Russia: Current Approaches and Issues in Methodology

Bodrunova

2020

The Palgrave Handbook of Digital Russia Studies

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Topic modeling as an instrument of probabilistic clustering for text collections has gained particular attention within the computational social science in Russia. This chapter looks at how topic modeling techniques have been developed and employed by the Russian scholars, both for Russian and other languages. We divide the works on topic modeling into methodological, applied, relational, and those dedicated to modeling quality assessment. While the methodological studies are the most developed, the works explaining the substance of the Russian-language discussions cover an important niche in political and social science. However, there is a gap between method-oriented works that treat Russian as “language as such” and the works by computational linguists who focus on Russian but treat topic modeling as a method of secondary importance.

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Section: Topic Modeling For the Russian Twittermentioning

confidence: 99%

Section: Quality Assessment and Interpretability Of The Russian-langumentioning

confidence: 99%

Section: Quality Assessment and Interpretability Of The Russian-langumentioning

confidence: 99%

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Topic Modeling in Russia: Current Approaches and Issues in Methodology

Bodrunova

2020

The Palgrave Handbook of Digital Russia Studies

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“…Raw data that are collected from social media provide additional challenges in terms of topic detection, due to their high noise, post length variations, author-induced grammar and lexicon distortions, and user-by-user dialogue fragmentation. The problem is especially significant for short user texts, like tweets [9]. Thus, before applying the model to data from social media for social science tasks, we used more structured real-world datasets for our experiment; these datasets had to be pre-labeled, as our goal was to test the method.…”

Section: Text Classification Vs Text Clustering: Approaches To Automa...mentioning

confidence: 99%

Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment

et al. 2020

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The paper is dedicated to solving the problem of optimal text classification in the areaof automated detection of typology of texts. In conventional approaches to topicality-based textclassification (including topic modeling), the number of clusters is to be set up by the scholar, andthe optimal number of clusters, as well as the quality of the model that designates proximity oftexts to each other, remain unresolved questions. We propose a novel approach to the automateddefinition of the optimal number of clusters that also incorporates an assessment of word proximityof texts, combined with text encoding model that is based on the system of sentence embeddings.Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerativehierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering.The preferred number of clusters is determined based on the “e-2” hypothesis. We set up anexperiment on two datasets of real-world labeled data: News20 and BBC. The proposed model istested against more traditional text representation methods, like bag-of-words and word2vec, to showthat it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models withdifferent encoding methods. We use three quality metrics to demonstrate that clustering quality doesnot drop when the number of clusters grows. Thus, we get close to the convergence of text clusteringand text classification.

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“…However, topic modeling, despite its wide area of application, has many well-known disadvantages. Among them is topic instability [5][6][7], difficulties with interpretation [8], artifacts of topic extraction [9], and low quality of topicality extraction for very short texts that need to be pooled to receive distinguishable topics [10]. However, an even bigger problem lies in the fact that, for fast enough assessment of topicality, topic modeling may not fit, as it provides only hints to topics via top words but does not summarize the meaning of the texts relevant to a given topic.…”

Section: Introductionmentioning

confidence: 99%

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

2022

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Abstractive summarization is a technique that allows for extracting condensed meanings from long texts, with a variety of potential practical applications. Nonetheless, today’s abstractive summarization research is limited to testing the models on various types of data, which brings only marginal improvements and does not lead to massive practical employment of the method. In particular, abstractive summarization is not used for social media research, where it would be very useful for opinion and topic mining due to the complications that social media data create for other methods of textual analysis. Of all social media, Reddit is most frequently used for testing new neural models of text summarization on large-scale datasets in English, without further testing on real-world smaller-size data in various languages or from various other platforms. Moreover, for social media, summarizing pools of texts (one-author posts, comment threads, discussion cascades, etc.) may bring crucial results relevant for social studies, which have not yet been tested. However, the existing methods of abstractive summarization are not fine-tuned for social media data and have next-to-never been applied to data from platforms beyond Reddit, nor for comments or non-English user texts. We address these research gaps by fine-tuning the newest Transformer-based neural network models LongFormer and T5 and testing them against BART, and on real-world data from Reddit, with improvements of up to 2%. Then, we apply the best model (fine-tuned T5) to pools of comments from Reddit and assess the similarity of post and comment summarizations. Further, to overcome the 500-token limitation of T5 for analyzing social media pools that are usually bigger, we apply LongFormer Large and T5 Large to pools of tweets from a large-scale discussion on the Charlie Hebdo massacre in three languages and prove that pool summarizations may be used for detecting micro-shifts in agendas of networked discussions. Our results show, however, that additional learning is definitely needed for German and French, as the results for these languages are non-satisfactory, and more fine-tuning is needed even in English for Twitter data. Thus, we show that a ‘one-for-all’ neural-network summarization model is still impossible to reach, while fine-tuning for platform affordances works well. We also show that fine-tuned T5 works best for small-scale social media data, but LongFormer is helpful for larger-scale pool summarizations.

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Topics in the Russian Twitter and Relations between their Interpretability and Sentiment

Cited by 9 publications

References 34 publications

Topic Modeling in Russia: Current Approaches and Issues in Methodology

Topic Modeling in Russia: Current Approaches and Issues in Methodology

Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment

Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages

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