Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets

Chong, Wei Yen; Selvaretnam, Bhawani; Soon, Lay-Ki

doi:10.1109/icaiet.2014.43

Cited by 48 publications

(18 citation statements)

References 8 publications

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“…Performing SA on Tweets associated with wildfires allows us to plot sentimental arcs for these events, as has been done with previous case studies [8]. In this work, we implement SA via ML approaches using Natural Language Processing [18,41,42]. This approach applies ML algorithms to the linguistic features of text data [43][44][45].…”

Section: Sentimental Wildfire: Overviewmentioning

confidence: 99%

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Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Lever

Arcucci

2022

J Comput Soc Sc

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The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

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Section: Sentimental Wildfire: Overviewmentioning

confidence: 99%

“…Social SA converts qualitative social text data into quantitative metrics which can then be used as social characteristics for a dataset of wildfire events. SA is performed using Natural Language Processing [41,42]. This approach applies ML algorithms to the linguistic features of text data [43][44][45].…”

Section: Sentimental Wildfires: Social Sentiment Analysismentioning

confidence: 99%

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Lever

Arcucci

2022

J Comput Soc Sc

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“…In this work, we focus on the pre/post-processing operators that are listed in Table 1 as they are commonly adopted in recent IRBL studies. In our work, we apply the default pre-processing (i.e., preBasic, the basic pre-processing operator for tokenization commonly used for natural language processing (NLP) tasks [36,37]) that is applied by all the techniques in the literature. Along with the basic operator, stop word removal (SWR) and stemming (STM) are widely chosen by several IRBL techniques [38,39,40,41,42].…”

Section: Pre/post-processing Operators For Irblmentioning

confidence: 99%

DigBug—Pre/post-processing operator selection for accurate bug localization

Kim

Ghatpande

Liu

et al. 2022

Journal of Systems and Software

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“…@ # $ & % dll. Karakter tersebut sebagian besar dapat memengaruhi akurasi proses analisis, karena bukan termasuk teks yang dapat ditemukan di kamus [18]. Penghapusan karakter menggunakan library regular expression milik python.…”

Section: Penghapusan Karakterunclassified

Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN)

Listyarini

Anggoro

2021

Jurnal Pendidikan dan Teknologi Indonesia

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Pemilihan kepala daerah 2020 menjadi kontroversi, sebab dilaksanakan ditengah pandemi covid-19. Komentar muncul di berbagai lini media sosial seperti twitter. Banyak masyarakat yang setuju pilkada dilanjutkan, namun banyak juga yang perpendapat untuk menunda pilkada sampai masa pandemi berakhir. Melihat perbedaan pendapat seperti ini, perlu dilakukan analisis sentimen, dengan tujuan untuk memperoleh persepsi atau gambaran umum masyarakat terhadap penyelenggaraan pilkada 2020 saat pandemi covid-19. Sebanyak 500 tweet diperoleh dengan cara crawling data dari twitter API menggunakan library tweepy, bedasarkan keyword yang telah ditentukan. Dataset yang didapat diberi label ke dalam dua kelas, negatif dan positif. Penelitian ini mengusulkan pendekatan deep learning dengan algoritma Convolution Neural Network (CNN) untuk klasifikasi, yang terbukti efektif untuk tugas Natural Language Processing (NLP) dan mampu mencapai kinerja yang baik dalam klasifikasi kalimat. Percobaan dilakukan dengan menerapkan 4-layer convolutional dan mengamati pengaruh jumlah epoch terhadap akurasi model. Variasi epoch yang digunakan adalah 50, 75, 100. Hasil dari penelitian menunjukkan bahwa, metode CNN dengan dataset pilkada ditengah pandemi mendapatkan akurasi tertinggi sebesar 90% dengan 4-layer convolutional dan 100 epoch. Didapatkan pula bahwa, semakin banyak epoch yang digunakan dalam model, akurasi cenderung meningkat.

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Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets

Cited by 48 publications

References 8 publications

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

DigBug—Pre/post-processing operator selection for accurate bug localization

Analisis Sentimen Pilkada di Tengah Pandemi Covid-19 Menggunakan Convolution Neural Network (CNN)

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