The application of k-nearest neighbors classifier for sentiment analysis of PT PLN (Persero) twitter account service quality

Damarta, R.; Hidayat, Akik; Abdullah, Atje Setiawan

doi:10.1088/1742-6596/1722/1/012002

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…An emotion dictionary is established based on the relationship between words and emoticons, then the emotional features are extracted from the dictionary, and then combined with other features to complete the emotion analysis of Weibo [13]. Before the emotional analysis of microblogs, the established subjective word dictionary is used to classify microblogs subjectively and objectively, and then to judge the emotional orientation of microblogs [14]. is paper uses two features: meta-information and grammatical information of microblog.…”

Section: Related Workmentioning

confidence: 99%

“…For each sentence; in res ["sentences"] do (7) e sentiment classification of each sentence in the social comment data was calculated (8) Count sentiment (sentencei, active, passive, neutrality) (9) If (passive is 0) and (neutrality>2 * active) then (10) Neutrality_ count f-neutrality_ count + 1 (11) If (passive is 0) and (neutrality <2 * active) then (12) Hot topics positve_count active_cou + 1 (13) If active >2 * passive then (14) Active_cou active_cou + 1 (15) Else (16) Passive_cou f-passive_cou + 1 (17) Return active_cou, passive_cou, neutrality_count largest modularity, which may be the reason why it achieves the best result in micro blog sentiment analysis. Combined with Figure 5 and Table 3, it can be found that both the number of communities and the degree of modules will affect the accuracy of micro blog sentiment analysis.…”

Section: Example Verificationmentioning

confidence: 99%

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Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

Yana

2022

Mathematical Problems in Engineering

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In social network, users can manage their social network and social identity, publish information on various topics, and obtain information published by other users through friend relationship. The resulting large amount of text data attract more and more scholars to study it. Text sentiment analysis has become a hot spot in social network data analysis and has important application value in academic field, social field, and business field. Based on the idea of pre-training, this paper improves the random word masking algorithm of deep pretraining task in the BERT (Bidirectional Encoder Representation) model to improve the efficiency and stability of model pretraining. Second, a new pretraining task of original sentence judgment is designed to enable the model to measure the degree of sentence smoothness, so that the BERT model can better understand the semantics of context. By referring to the idea of attention mechanism, a deep learning framework with attention weight added into gated convolution is constructed and the special attention weight method is adopted to enhance semantic information. Second, gating convolution and attention mechanism are combined to model aspect-related semantic information and text complete semantic information. Finally, classify the emotion classifier layer of social network, use Softmax function to complete negative, positive, and neutral multiple classifications and calculate the result of emotion classification. By applying the optimized convolutional neural network cyclic optimization network to single task and multitask in practice, the feasibility of applying the optimized convolutional neural network and cyclic neural network to social network sentiment analysis is verified.

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Section: Related Workmentioning

confidence: 99%

Section: Example Verificationmentioning

confidence: 99%

Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

Yana

2022

Mathematical Problems in Engineering

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“…Media sosial juga menjadi media yang mampu menunjang kinerja perusahaan dalam hal penyampaian informasi, perbaikan pelayanan, hingga strategi promosi seperti yang dapat dilakukan pada perusahaan penyedia layanan ketenagalistrikan. Opini masyarakat terhadap pelayanan ketenagalistrikan saat ini sebagian besar disampaikan melalui media sosial salah satunya adalah akun mikro blog Twitter [1]. Berdasarkan ulasan masyarakat tersebut maka manajemen penyedia layanan ketenagalistrikan dapat mengetahui sentimen masyarakat terhadap kinerja pelayanannya.…”

Section: Pendahuluanunclassified

Analisis Opini Pengguna Aplikasi New PLN Mobile Menggunakan Text Mining

Tambunan

Hapsari

2021

petir

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Media sosial pada saat ini menjadi suatu media yang sangat populer untuk menyampaikan opini oleh masyarakat di Indonesia. Melalui media sosial pengguna dapat dengan mudah mengungkapkan pengalamannya terhadap suatu produk, salah satunya adalah aplikasi New PLN Mobile dari PT PLN (Persero). Aplikasi tersebut menjadi platform digital untuk memenuhi berbagai kebutuhan pelanggan terkait pelayanan ketenagalistrikan. Salah satu metode yang dapat dipakai untuk menganalisa opini pengguna adalah menggunakan text mining dengan pendekatan word cloud, network explorer, jenis emosi, dan analisis sentimen. Hasil penelitian menunjukkan bahwa analisis word cloud memberikan frekuensi komentar terkait keberadaan aplikasi, pengalaman pelanggan, fitur baru, informasi pelanggan, hingga interaksi dan komunikasi pada media sosial. Selanjutnya analisis network explorer menunjukkan bahwa kata yang saling berkaitan adalah “aplikasi pln mobile” dan “kemudahan layanan gangguan”. Analisa jenis emosi mengekspresikan sebagian besar pelanggan terkejut (surprise) dengan adanya aplikasi aplikasi New PLN Mobile. Analisis sentimen menunjukkan bahwa sebagian besar kluster pelanggan menunjukkan sentimen yang sangat positif terhadap keberadaan aplikasi New PLN Mobile.

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“…Compared to existing blogging platforms, Twitter's response time is quicker. This emotional analysis has been used by vendors to gain insight into their business activities to recognize new trends on markets (Damarta et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Emotion-Based Sentiment Analysis Using Conv-BiLSTM with Frog Leap Algorithms

Yelisetti¹,

Geethanjali²

2023

AIP

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Social media, blogs, review sites and forums can produce large volumes of data in the form of users' emotions, views, arguments and opinions about various political events, brands, products and social problems. The user's sentiment expressed on the web influences readers, politicians and product vendors. These unstructured social media data are analysed to form structured data, and for this reason sentiment analysis has recently received the most important research attention. Sentiment analysis is a process of classifying the user's feelings in different manners such as positive, negative or both. The major issue of sentiment analysis is insufficient data processing and outcome prediction. For this, deep learning-based approaches are effective due to their autonomous learning ability. Emotion identification from the text in natural language processing (NLP) provides more benefits in the field of e-commerce and business environments. In this paper, emotion detection-based text classification is used for sentiment analysis. The data collected are pre-processed using tokenization, stop word discarding, stemming and lemmatization. After performing data pre-processing, the features are identified using term frequency and inverse document frequency (TF-IDF). Then the filtered features are turned into word embeddings by documents as a vector (Doc2Vec). Then, for text classification, a deep learning (DL) based model called convolutional bidirectional long short-term memory (CBLSTM) is used to differentiate the sentiments of human expression into positive or good and negative or bad emotions. The neural network hyper-parameters are optimized with a meta-heuristic algorithm called the frog leap approach (FLA). The proposed CBLSTM with FLA uses four review and Twitter datasets. The experimental results of this study are compared with the conventional approaches LSTM-RNN and LSTM-CNN to prove the efficiency of the proposed model. Compared to LSTM-RNN and LSTM-CNN, the proposed model secures an improved average accuracy of 98.1% for review datasets and 97.5% for Twitter datasets.

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The application of k-nearest neighbors classifier for sentiment analysis of PT PLN (Persero) twitter account service quality

Cited by 6 publications

References 5 publications

Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

Analisis Opini Pengguna Aplikasi New PLN Mobile Menggunakan Text Mining

Emotion-Based Sentiment Analysis Using Conv-BiLSTM with Frog Leap Algorithms

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