This poster introduces a new tool named Apollo which analyzes textual information in the geo-tagged twitter streams of trending hashtags using sliding time window. It performs sentiment analysis as well as emotion detection of the opinions of the masses about a trending world wide topic such as #COVID-19, #ClimateChange, #Black-LivesMatter, etc. based on Knowledge Graphs. Apollo currently pro- vides an interactive visualization of the analysis of the trending hashtag #COVID-19

Alam, M.

Kaschura, M.

Sack, H.

English

KITopen

Apollo: Twitter Stream Analyzer of TrendingHashtags: A Case-study of #COVID-19Mehwish Alam1,2, Manuel Kaschura2, and Harald Sack1,21 FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany2 Karlsruhe Institute of Technology, Institute AIFB, Germany{mehwish.alam,harald.sack}@fiz-karlsruhe.de,manuel.kaschura@student.kit.eduAbstract. This poster introduces a new tool named Apollo which an-alyzes textual information in the geo-tagged twitter streams of trendinghashtags using sliding time window. It performs sentiment analysis aswell as emotion detection of the opinions of the masses about a trend-ing world wide topic such as #COVID-19, #ClimateChange, #Black-LivesMatter, etc. based on Knowledge Graphs. Apollo currently pro-vides an interactive visualization of the analysis of the trending hashtag#COVID-19.Copyright © 2020 for this paper by its authors. Use permitted under Creative Com-mons License Attribution 4.0 International (CC BY 4.0).1 IntroductionSocial Media has become one of the most effective ways for the populations tovoice their opinions about ongoing major political, health, or any other kindof phenomena. Twitter is one of the popular social media channels where theusers utilize hashtags for increasing the presence of their opinions about ongoingsituations such as #COVID-19, #BlackLivesMatter, #ClimateChange, etc. Thecurrent global challenge faced by humanity is the COVID-19 pandemic. A hugeamount of tweets are published each day targeting the related topics such asdeath rates, recovered cases, mental health issues, etc. These streaming twit-ter data can be used for many purposes such as monitoring mental health ofthe people during the lockdown, sentiments of the people suffering/recoveringfrom the pandemic, agitation towards the measures taken by the governmentalauthorities for COVID-19 prevention, etc.This paper particularly focuses on introducing a tool Apollo3 which analysesthe stream of English tweets related to #COVID-19. So far many other effortshave been put forth for analyzing the Twitter data. One of the early datasets [3]contains about 5 million tweets related to COVID-19, however, it only consists ofthe raw data. TweetKB [4] is another knowledge-base which performs sentimentanalysis, entity linking, etc. of the tweets from October, 2019 to April, 2020 over3 http://covid-twitter-stream.fiz-karlsruhe.de/8 million tweets. In [6], the authors perform sentiment classification using deeplearning methods over tweets. However, none of the above reported studies havetaken into account the stream of tweets. Due to huge amount of tweets beingposted every minute all over the world, it is a challenge to process and analyzethis information manually. This leads to the compulsion of introducing novelalgorithms for automated analysis.Apollo processes these twitter streams using sliding time window, retrievesgeo-referenced tweets on the run and performs knowledge-aware sentiment anal-ysis and emotion detection of the masses about the pandemic as well as framedetection over tweets using Framester [5].2 Knowledge-Aware Twitter Stream Sentiment AnalysisHandling Twitter Streams. In order to handle twitter streams, time-basedsliding window was used. A sliding window defines a time span with a fixedduration which goes back in time for this defined time span starting from currenttime. For instance, a sliding window of two minutes includes any events thathave occurred in the past two minutes. This work uses Twitter Stream API 4 forobtaining twitter stream pertaining to particular hashtag such as #COVID-19and variations of this hashtag such as “corona”, “coronavirus”, etc. The timespan used for the current implementation is 30 minutes. The reason behindchoosing 30 minute window is that it is extremely expensive to stream andprocess data over larger sliding time windows.Pre-Processing Tweets. In the preprocessing step, hashtags were processedusing word segmenter5. If the hashtag consists of multiple words, e.g., #black-livesmatter, it was segmented into “black lives matter”. The user mentions aswell as URLs were removed because they are not used in the current analysis.For the retweets, the prefix “RT:” was removed. Emojis were mapped to theappropriate emotions manually. These mappings are available online6.Processing Geo-tagged Tweets. The locations of the tweets were convertedto their co-ordinates using Geopy7 which were later on used for plotting the emo-tions on the globe. For 30 minutes of streaming, more than 40,000 tweets wereretrieved out of which 236 mentioned a place, 8 mentioned their co-ordinates,and 28002 contained location information. Only 0.03% of the tweets had theirco-ordinates which led to the necessity of using external service such as Geopy.However, one of the bottle necks of Geopy is that it has limited number ofrequests per minute and for each request it takes 10-20 seconds which led todelays in the computation. In future, Apollo will use paid services for betterperformance. Another problem encountered was that most of the tweets did4 http://docs.tweepy.org/en/latest/streaming_how_to.html5 https://pypi.org/project/wordsegment/6 https://github.com/ISE-FIZKarlsruhe/TwitterStreamAnalysis7 https://geopy.readthedocs.io/en/stable/not contain location information, or had locations such as planet earth, paralleluniverse, the emoticon of a globe, etc.Processing Full Text. Full text of each of the tweets was processed by per-forming Word Sense Disambiguation (WSD). Several algorithms have been pro-posed so far but Apollo currently uses Lesk algorithm. Other algorithms suchas UKB and Babelfy will be used for future releases. After obtaining the Word-Net synsets, Framester was used for linking the tweets to the whole linked dataresources (linguistic or otherwise) contained in Framester.Framester is a large linguistic linked open data including about 30 millionRDF triples acting as a hub between FrameNet, WordNet, VerbNet, BabelNet,Predicate Matrix, etc. It leverages this wealth of links to create an interoperableand homogeneous predicate space formally represented using frame semantics.Framester uses a mapping between WordNet, BabelNet, VerbNet and FrameNetat its core using detour based approach, expands it to other linguistic resourcestransitively, and represents all of this formally. It further links these resourcesto important external ontological and linked data resources such as DBpedia,YAGO, DOLCE-Zero, schema.org, NELL, etc. Further links to DeepKnowNettopic signatures, as well as SentiWordNet [2] and DepecheMood [7] mood map-pings, are also available. Framester has been successfully used in downstreamtask such as knowledge reconciliation using frame embeddings [1].Fig. 1. Framester - Linguistic Linked Data HubVisualizing People’s Sentiments and Emotions. The mappings containedin Framester about SentiWordNet were used to assign sentiment scores to thetweets. The overall score for the whole sentence was computed by averaging in-dividual sentiment scores. In the presence of an emoji, a score of +0.5 or -0.5 wasadded to the numerator of the average and the denominator was increased by1 (in case of one emoji). Similarly, for emotion detection the mappings betweenWordNet and DepecheMood (unique to Framester) were used. Finally, the aggre-gated score for each co-ordinate was visualized on 3-D globe which provides aninteractive interface including zoom-in, zoom-out, etc. Figure 2 shows the finalvisualization of the sentiments in Apollo. The legends on the left side describethe sentiments or emotions. The current version of Apollo only considers theEnglish tweets which causes some parts of the globe to be darker meaning thatthere is only small amount of data to be analyzed due to higher frequencies ofnon-English tweets.(a) Sentiments of masses about COVID-19 (b) Emotions of masses about COVID-19Fig. 2. Interface of Apollo for visualizing sentiments (+ve and -ve) and emotions ofthe masses around the globe for a 30 minutes sliding window about #COVID-19Word Frame Disambiguation. Currently, Apollo also enables the downloadswhich include the mappings of the tweets to FrameNet frames according to theTransX mappings between WordNet and FrameNet as provided by Framester(using skos:closeMatch). This further connects the tweets to other externalresources in Framester such as DBpedia, DOLCE, etc. This process is referred toas Word Frame Disambiguation. Further mappings can be obtained by crawlingFramester which is available through SPARQL endpoint8.3 Future DirectionsIn future, Apollo will be able to automatically extract trending hashtags. Dif-ferent WSD algorithms such as UKB or Babelfy will be implemented. Twitterstream classification using machine learning algorithms for providing real-timeanalysis over larger time spans will also be implemented.References1. Alam, M., Recupero, D.R., Mongiov̀ı, M., Gangemi, A., Ristoski, P.: Event-basedknowledge reconciliation using frame embeddings and frame similarity. Knowl.Based Syst. 135, 192–203 (2017). https://doi.org/10.1016/j.knosys.2017.08.014,https://doi.org/10.1016/j.knosys.2017.08.0148 http://etna.istc.cnr.it/framester2/sparql2. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: An enhanced lexical re-source for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K.,Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.)Proceedings of the International Conference on Language Resources and Evalua-tion, LREC 2010, 17-23 May 2010, Valletta, Malta. European Language ResourcesAssociation (2010)3. Chen, E., Lerman, K., Ferrara, E.: Tracking social media discourse about thecovid-19 pandemic: Development of a public coronavirus twitter data set. CoRRabs/2003.07372 (2020), https://arxiv.org/abs/2003.073724. Dimitrov, D., Baran, E., Fafalios, P., Yu, R., Zhu, X., Zloch, M., Dietze, S.:Tweetscov19 - A knowledge base of semantically annotated tweets about theCOVID-19 pandemic. CoRR abs/2006.14492 (2020), https://arxiv.org/abs/2006.144925. Gangemi, A., Alam, M., Asprino, L., Presutti, V., Recupero, D.R.: Framester: Awide coverage linguistic linked data hub. In: Blomqvist, E., Ciancarini, P., Poggi, F.,Vitali, F. (eds.) Knowledge Engineering and Knowledge Management - 20th Interna-tional Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings.Lecture Notes in Computer Science, vol. 10024, pp. 239–254 (2016)6. Li, I., Li, Y., Li, T., Álvarez-Napagao, S., Garćıa, D.: What are we depressed aboutwhen we talk about COVID19: mental health analysis on tweets using natural lan-guage processing. CoRR abs/2004.10899 (2020), https://arxiv.org/abs/2004.108997. Staiano, J., Guerini, M.: Depeche mood: a lexicon for emotion analysis from crowdannotated news. In: Proceedings of the 52nd Annual Meeting of the Association forComputational Linguistics, ACL 2014, June 22-27, 2014, Baltimore, MD, USA, Vol-ume 2: Short Papers. pp. 427–433. The Association for Computer Linguistics (2014).https://doi.org/10.3115/v1/p14-2070, https://doi.org/10.3115/v1/p14-2070

Apollo: Twitter stream analyzer of trending hashtags: A case-study of #COVID-19

https://publikationen.bibliothek.kit.edu/1000127039/97523179

Apollo: Twitter stream analyzer of trending hashtags: A case-study of #COVID-19

Abstract

Similar works

Full text

Available Versions

KITopen