Text-based sentiment analysis in finance: synthesising the existing literature and exploring future directions

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Advances in Deep Learning have drastically improved the abilities of Natural Language Processing (NLP) research, creating new state-of-the-art benchmarks. Two research streams at the forefront of NLP analysis are transformer architecture and multimodal analysis. This paper critically evaluates the extant literature applying sentiment analysis techniques to the financial domain. We classify the financial sentiment analysis literature according to the most used techniques in the area, with a focus on methods used to detect sentiment within corporate earnings conference calls, because of their dual modality (text-audio) nature. We find that the financial literature follows a similar path to NLP sentiment literature, in that more advanced techniques to define sentiment are being used as the field progresses. However, techniques used to determine financial sentiment currently fall behind state-of-the-art techniques used within NLP. Two future directions stem from this paper. Firstly, we propose that the adoption of transformer architecture to create robust representations of textual data could enhance sentiment analysis in academic finance. Secondly, the adoption of multimodal classifiers in finance represents a new, currently underexplored area of study that offers opportunities for finance research.
Original languageEnglish
Article numbere1549
Number of pages17
JournalIntelligent Systems in Accounting, Finance and Management
Issue number1
Early online date25 Feb 2024
Publication statusPublished - 31 Mar 2024


  • sentiment analysis
  • earnings conference calls
  • multimodal sentiment classifier
  • transformer architecture
  • text analysis
  • natural language processing
  • natural language understanding


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