Combining SentiStrength and Multilayer Perceptron in Twitter Sentiment Classification

Prastowo, Eko Yudhi; Endroyono,; Yuniarno, Eko Mulyanto

doi:10.1109/isitia.2019.8937134

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…amplifiers, maximizers, downtoners, negation). When comparing the accuracy of our approach to the widely used sentiment analysis tool SentiStrength [61] (see, e.g., [22,52,55]), we achieved a better classification of negative emotions (0.85 vs. 0.73) but a comparatively worse accuracy for positive emotions (0.54 vs 0.61). However, also note that such a comparison is biased to a certain degree, because the detection and classification of positive emotions is a more difficult task than classifying negative emotions (see, e.g., [61]) and SentiStrength only identifies the emotional valence (positive or negative) of a text, while our approach explicitly identifies the eight basic emotions found in Plutchik's wheel of emotions.…”

Section: Methodsmentioning

confidence: 96%

“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

Kušen

Strembeck

2020

J Comput Soc Sc

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In this paper, we analyze more than 16 million tweets that have been sent from 6.1 million Twitter accounts and are related to nine natural disasters. As part of our analysis, we identify eight basic emotions conveyed in these tweets. We found that during natural disasters, social media messages at first predominantly express fear, while sadness and positive emotions increase in the immediate aftermath of the event. In this context, positive emotions contribute to the social phenomenon of emotional bonding and are often related to compassion, gratitude, as well as donations for disaster relief. In our analysis, we found that the users’ emotional expressions directly contribute to the emergence of the underlying communication network. In particular, we identified statistically significant structural patterns that we call emotion-exchange motifs and show that: (1) the motifs 021U and 021D are common for the communication of all eight emotions considered in this study, (2) motifs which include bidirectional edges (i.e. online conversations) are generally not characteristic for the communication of surprise, sadness, and disgust, (3) the structural analysis of a set of emotions (rather than a single emotion) leads to the formation of more complex motifs representing more complex social interactions, and (4) the messaging patterns emerging from the communication of joy and sadness show the highest structural similarity, even reaching a perfect similarity score at some point during the data-extraction period.

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Section: Methodsmentioning

confidence: 96%

“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

Kušen

Strembeck

2020

J Comput Soc Sc

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“…This shows how SA is an effective mode of longitudinal monitoring of CS. SA was carried out automatically with the Sentistrength software (Prastowo & Yuniarno, 2019). SentiStrength is, therefore, a tool that allows us to calculate the sentiment and the analysis of the text directly from the internal function of the computer prompt commands, as shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…The mode of operation of Sentistrength is to make a match between the input text, then the tweets of Trenitalia users collected in a file .csv, and the inner vocabulary. Technically, the operation of the software is shown in Figure 2, where the modelling of Prastowo and Yuniarno (2019) is reported. Specifically, the model represents the representation of a training phase of the software.…”

Section: Methodsmentioning

confidence: 99%

SentiSfaction: New cultural way to measure tourist COVID-19 mobility in Italy

Papapicco

2023

MEDITERR J SOC BEH RES

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From a psycho-linguistic and marketing perspective, the research fits into the evaluation of in the context of tourism and, in particular, tourism mobility, targeting one of the leading Italian rail transport companies, namely Trenitalia. This study, conducted on tweets, aims to examine how talks about the transport service offered by Trenitalia. A total of 674 tweets for the tourist season 2019 and 100 tweets for the tourist season 2020 were collected following the pre-COVID-19 and COVID-19 period. The methodology is the application of sentiment analysis (SA) that produces quantitative and qualitative results. For the quantitative part, the sentiment was calculated first automatically via the Sentistrength software, then an extraction of the frequencies and calculation of the dependence (Chi-square statistic and t-test) between year and polarity was conducted with R, statistical software. The results show that SA is a good methodology of analysis of the online reputation and customer satisfaction of a company that deals with tourism, also in the difference between pre-COVID-19 and COVID-19 period.

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Analysis of the Effects Using BERT Feature Extraction Method on Case of Sentiment Classification on Twitter Social Media

Prakoso,

Hermawan,

Efendi

2023

2023 1st IEEE International Conference on Smart Technology (ICE-SMARTec)

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Combining SentiStrength and Multilayer Perceptron in Twitter Sentiment Classification

Cited by 4 publications

References 13 publications

“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

SentiSfaction: New cultural way to measure tourist COVID-19 mobility in Italy

Analysis of the Effects Using BERT Feature Extraction Method on Case of Sentiment Classification on Twitter Social Media

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