Sentiment Analysis Dataset of Seblak Ndjedir Reviews

Description

Description: This dataset contains customer reviews of Seblak Ndjedir collected from digital platforms, particularly Google Maps and other online review portals. The dataset focuses on Indonesian food business reviews and is intended for research in Natural Language Processing (NLP), especially for sentiment analysis and text classification tasks. It includes reviews written in Bahasa Indonesia that reflect customer opinions on taste, price, service quality, and overall dining experience. The data has been anonymized to protect personal information and is provided in a clean CSV format suitable for preprocessing, analysis, and machine learning model training. Researchers and practitioners can use this dataset to: Develop sentiment analysis models. Perform text classification tasks. Conduct opinion mining on food-related reviews. Evaluate machine learning and NLP models on Indonesian customer-generated text. Dataset Details: Language: Indonesian Format: CSV Number of Reviews: [insert number of rows in your CSV] Fields Included: review_text, sentiment_label (optional), rating (optional) Usage License: CC BY 4.0 Keywords: Indonesian, Customer Reviews, Seblak Ndjedir, Sentiment Analysis, NLP, Text Classification.

Steps to reproduce

How to Use the Dataset: Download the dataset: Obtain the CSV file from the Mendeley Data repository. Load the dataset: Use Python, R, or any preferred data analysis tool to load the CSV file. For example, in Python: import pandas as pd data = pd.read_csv("seblak_ndjedir_reviews.csv") Explore the dataset: Examine the structure, fields, and sample reviews: print(data.head()) print(data.info()) Preprocess the text (optional): Remove punctuation, emojis, or special characters. Normalize text (lowercasing, stemming, or lemmatization if needed). Remove stopwords in Bahasa Indonesia. Tokenize sentences or words using a tokenizer compatible with Indonesian NLP models. Use the dataset for NLP tasks: For sentiment analysis: classify reviews into positive, neutral, or negative categories. For text classification: categorize reviews based on service, taste, or price. For opinion mining: extract customer feedback insights. Evaluate models: Use metrics such as accuracy, precision, recall, F1-score, or confusion matrix to assess the performance of classification models.

Institutions

• Universitas Islam Negeri Maulana Malik Ibrahim

  East Java, Malang

Categories

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