Study of BP's Energy Outlook Through Corpus Software Antconc

The corpus-based software AntConc, developed over nearly 25 years, has found widespread use in linguistics, translation, foreign language teaching, and literary studies, providing valuable support for researchers in text analysis. This study primarily utilizes AntConc 4.3.0, along with other tools such as TreeTagger (a tool used for part-of-speech) and PatternBuilder (an aid for regular expressions in searching POS-tagged English texts) to create a corpus, with the total of 61,618 tokens, analyzing BP's Energy Outlook in the past three years. The focus is on examining word frequency, collocation, and KWIC (Key Word in Context) to identify patterns within the report. By exploring these linguistic features, the study aims to shed light on the linguistic tendencies and characteristics of the Outlook, offering deeper insights that help readers better understand and interpret the entire document.

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Published
2025-04-18
DOI
https://doi.org/10.1145/3746469.3746578
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Study of BP's Energy Outlook Through Corpus Software Antconc

Ying Sun, Ke Chen
Machine Learning in Materials Science
article

Study of BP's Energy Outlook Through Corpus Software Antconc

Ying Sun, Ke Chen
article en

Abstract

The corpus-based software AntConc, developed over nearly 25 years, has found widespread use in linguistics, translation, foreign language teaching, and literary studies, providing valuable support for researchers in text analysis. This study primarily utilizes AntConc 4.3.0, along with other tools such as TreeTagger (a tool used for part-of-speech) and PatternBuilder (an aid for regular expressions in searching POS-tagged English texts) to create a corpus, with the total of 61,618 tokens, analyzing BP's Energy Outlook in the past three years. The focus is on examining word frequency, collocation, and KWIC (Key Word in Context) to identify patterns within the report. By exploring these linguistic features, the study aims to shed light on the linguistic tendencies and characteristics of the Outlook, offering deeper insights that help readers better understand and interpret the entire document.

Xi'an Shiyou University (CN)
Xi'an Shiyou University
Openalex Percentile: Top 82%
Machine Learning in Materials Science
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