Deteksi Kondisi Emosi pada Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing

Rohman, Arif; Utami, Ema; Raharjo, Suwanto

doi:10.30864/eksplora.v9i1.277

Cited by 12 publications

(14 citation statements)

References 8 publications

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“…The word normalizer feature is used to correct words in sentences, so that a good and correct sentence is produced according to grammar rules [20]. Conversation "selamat pagi kak ada yang bisa dibantu" then after the Word Normalizer process becomes "salam kakak ada yang bisa dibantu".…”

Section: − Word Normalizermentioning

confidence: 99%

Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach

2021

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Customer satisfaction is very important for public service providers, customer satisfaction can be delivered with a survey application or writing criticism that can be used to evaluate and improve service. Unfortunately, there are only a few customers who are willing to give an assessment. The survey application cannot represent the overall feeling of the customer, so it is necessary to analyze the content of the conversation between the customer and the service personnel to determine the level of customer satisfaction. In small amounts, it can be done manually, but in large quantities it is more effective to use the system. A solution is needed in the form of a system that converts voice conversations into text and analyzes customer satisfaction to obtain information for evaluation and improvement of services. This research uses Knearest neighbors (KNN) and term frequency-inverse document frequency (TF-IDF) algorithm with natural language processing (NLP) approach to classify conversations into 2 classes, "satisfied" and " dissatisfied ". The results of this study received 74.00% accuracy, 76.00% precision and 73.08% recall. In conversations with the label "satisfied" shows customers satisfied with the service and fulfillment of customer desires, while in conversations with the label "not satisfied" customers are less satisfied with the waiting time.

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Section: − Word Normalizermentioning

confidence: 99%

Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach

2021

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“…Some euphemisms are intended to amuse; while others use bland, inoffensive terms for things the users wishes to downplay. Rickey in (Ayu et al, 2017) Data Emotions affect daily human life such as social relationships, memory, and even decision making Hamdi in (Rohman et al, 2019). Data 3 explained offensive something unpleasant.…”

Section: Euphemismsmentioning

confidence: 99%

The Emotional Lexicon Used by Male and Female Communication: Study of Balinese Language Used in South Kuta-Bali

Putri

Santika

2020

LEEA

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The aims of this research is to analyse about the differences of emotional lexicon used by male and female communication in South Kuta-Bali when they used Balinese language in their daily interaction. The scope of male and female is closely related to the social behavior which includes the social identity of male and female in society and this becomes the basis of how the language is used in this context of social. This research is interested to uncover more how people use language in terms of expressing their emotional in social interaction. This study is a sociolinguistic approach used the theory from Hickey, Raymon (2010). The data source in this study is the south Kuta community who use Balinese language in social interactions. The Data collection is done by observation, interview, recording and note taking and descriptive qualitative method is applied to analyze the data. The result of the analysis found that the emotional lexical is used by the male and female in their social interaction, it could mention that both Augmentatives and Euphemisms is used by male and female in their social interaction however the augmentative is mostly used by female in informal occasion. Balinese female often used prohibition instead of imperative in expressing her idea about ordering someone to do something. In the other hand, the male directly used imperative sentence in ordering something. He usually does not use many awkwardness to say his point in a conversation. This may be considered that the male often go to the straight point when expressing his idea. Keywords: Emotional Lexicon, Male and Female

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“…NLP merupakan salah satu cabang ilmu AI yang berfokus pada pengolahan bahasa natural. Bahasa natural adalah bahasa yang secara umum digunakan oleh manusia dalam berkomunikasi satu sama lain [12]. Fitur NLP yang digunakan pada penelitian ini adalah: a. Membuang emoticon, angka dan simbol Komentar pada Instagram umumnya mengandung emoticon, pada penelitian ini emoticon dibuang karena makalah ini hanya membahas teks yang terkandung pada komentar.…”

Section: Loginunclassified

“…Komentar yang mengandung mention seperti "Mau dong @adamwicaksono55 @adamwahyudi_11" setelah proses penanganan mention menjadi "mau dong @mention @mention" d. Word Normalizer Word Normalizer digunakan untuk memperbaiki kata-kata dalam kalimat sehingga menghasilkan kalimat yang sangat baik dan benar sesuai dengan aturan tata bahasa. Peningkatan ini diperlukan untuk memudahkan pembaca memahami makna kalimat [12]. Komentar "Harga sdh termasuk ongkir kah" maka setelah proses Word Normalizer menjadi "harga sudah termasuk ongkir kah" .…”

Section: Loginunclassified

Analisis Komentar Potensial pada Social Commerce Instagram Menggunakan TF-IDF

Musyarofah¹,

Utami²,

Raharjo³

2020

eksplora

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Komentar di Instagram sangat berharga, informatif dan sangat membantu. Bagi penjual komentar adalah fitur yang menunjukkan respons pengguna Instagram terhadap produk yang ditawarkan, dan melalui fitur komentar penjual dapat menemukan pelanggan yang potensial. Manfaat tersebut diperoleh apabila penjual melakukan analisis pada komentar di toko Instagram-nya. Sangat dimungkinkan untuk menganalisis secara manual apabila data komentar pada tokonya berjumlah sedikit namun apabila komentar yang dimiliki banyak maka akan lebih cepat apabila menggunakan sistem. Banyaknya spam dapat mengganggu informasi yang ada pada komentar, sehingga tidak menjamin banyaknya komentar pada sebuah posting-an maka banyak pula yang ingin membeli produk tersebut. Oleh karena itu dibutuhkan sistem yang bisa memfilter komentar agar penjual dapat menemukan pelanggan yang tepat untuk produknya. Penelitian ini menggunakan algoritma TF-IDF untuk mengklasifikasikan komentar ke dalam 2 kelas (potensial dan tidak potensial) dan memperoleh akurasi sebesar 80%, presisi 0,76 dan recall 0,94. Berdasarkan hasil penelitian pada 294 komentar, 27% di antaranya adalah komentar tidak potensial. Kata yang menunjukkan minat beli seseorang adalah “berapa”, ”kak”, ”ada”, dan ”tidak”, sedangkan kata dominan pada komentar tidak potensial adalah kata “mention” yang menunjukkan aktivitas mention.

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Deteksi Kondisi Emosi pada Media Sosial Menggunakan Pendekatan Leksikon dan Natural Language Processing

Cited by 12 publications

References 8 publications

Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach

Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach

The Emotional Lexicon Used by Male and Female Communication: Study of Balinese Language Used in South Kuta-Bali

Analisis Komentar Potensial pada Social Commerce Instagram Menggunakan TF-IDF

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