Tweet congestion locations identification using natural language processing

Natural Language Processing (NLP) is a field of computer science and linguistics that explores how computers interact with human (natural) language. NLP is frequently seen as a subfield of artificial intelligence, and its research area overlaps with computational linguistics. Machine translation, na...

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Bibliographic Details
Main Authors: Subarkah, Aan, Kusnanto, Geri, Permai, Syarifah Diana, Ohyver, Margaretha, Arifin, Samsul
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Expert systems
Information retrieval
Language
Linguistics
Machine translation
Marking
Natural language processing
Speech recognition
Traffic congestion
User interfaces
Words (language)
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Summary:Natural Language Processing (NLP) is a field of computer science and linguistics that explores how computers interact with human (natural) language. NLP is frequently seen as a subfield of artificial intelligence, and its research area overlaps with computational linguistics. Machine translation, natural language text processing and summarization, user interfaces, speech recognition, and expert systems are all examples of NLP applications. Low-level NLP tasks and higher-level NLP tasks are two types of Natural Language Processing tasks. Tokenization, part-of-speech assignment to individual words (POS tagging), and shallow parsing are examples of low-level NLP activities (chunking). Meanwhile, higher-level NLP tasks, such as spelling/grammatical mistake detection and recovery, named entity recognition (NER), and information extraction, are constructed on top of low-level NLP tasks and used according to the problems found (IE). This study uses natural language processing to extract the location of congestion from tweets (twit). We get the location name and traffic conditions from the tweet by implementing a low-level NLP task in the form of tokenization, POS tagging, and chunking, followed by a higher-level NLP work in the form of named entity recognition. The types of words, such as nouns, verbs, adjectives, and descriptions, are taught at the introductory stage. Following POS tagging, word grouping (chunking) is carried out; if numerous consecutive words are verbs, they are classified as location names, whereas adjectives are grouped as traffic conditions.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0140149