Key-phrase boosted unsupervised summary generation for FinTech
organization
- URL: http://arxiv.org/abs/2310.10294v1
- Date: Mon, 16 Oct 2023 11:30:47 GMT
- Title: Key-phrase boosted unsupervised summary generation for FinTech
organization
- Authors: Aadit Deshpande, Shreya Goyal, Prateek Nagwanshi, Avinash Tripathy
- Abstract summary: Some of the NLP applications such as intent detection, sentiment classification, text summarization can help FinTech organizations to utilize the social media language data.
We design an unsupervised phrase-based summary generation from social media data, using 'Action-Object' pairs (intent phrases)
We evaluate the proposed method with other key-phrase based summary generation methods in the direction of contextual information of various Reddit discussion threads.
- Score: 4.583461218488076
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the recent advances in social media, the use of NLP techniques in social
media data analysis has become an emerging research direction. Business
organizations can particularly benefit from such an analysis of social media
discourse, providing an external perspective on consumer behavior. Some of the
NLP applications such as intent detection, sentiment classification, text
summarization can help FinTech organizations to utilize the social media
language data to find useful external insights and can be further utilized for
downstream NLP tasks. Particularly, a summary which highlights the intents and
sentiments of the users can be very useful for these organizations to get an
external perspective. This external perspective can help organizations to
better manage their products, offers, promotional campaigns, etc. However,
certain challenges, such as a lack of labeled domain-specific datasets impede
further exploration of these tasks in the FinTech domain. To overcome these
challenges, we design an unsupervised phrase-based summary generation from
social media data, using 'Action-Object' pairs (intent phrases). We evaluated
the proposed method with other key-phrase based summary generation methods in
the direction of contextual information of various Reddit discussion threads,
available in the different summaries. We introduce certain "Context Metrics"
such as the number of Unique words, Action-Object pairs, and Noun chunks to
evaluate the contextual information retrieved from the source text in these
phrase-based summaries. We demonstrate that our methods significantly
outperform the baseline on these metrics, thus providing a qualitative and
quantitative measure of their efficacy. Proposed framework has been leveraged
as a web utility portal hosted within Amex.
Related papers
- Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks.
We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement.
We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
arXiv Detail & Related papers (2024-07-04T16:41:08Z) - QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums [10.684484559041284]
This study introduces QuaLLM, a novel framework to analyze and extract quantitative insights from text data on online forums.
We applied this framework to analyze over one million comments from two Reddit's rideshare worker communities.
arXiv Detail & Related papers (2024-05-08T18:20:03Z) - Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs)
We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing.
We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z) - How Can Context Help? Exploring Joint Retrieval of Passage and
Personalized Context [39.334509280777425]
Motivated by the concept of personalized context-aware document-grounded conversational systems, we introduce the task of context-aware passage retrieval.
We propose a novel approach, Personalized Context-Aware Search (PCAS), that effectively harnesses contextual information during passage retrieval.
arXiv Detail & Related papers (2023-08-26T04:49:46Z) - Exploring Large Language Model for Graph Data Understanding in Online
Job Recommendations [63.19448893196642]
We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs.
By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
arXiv Detail & Related papers (2023-07-10T11:29:41Z) - Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases.
Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding.
This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
arXiv Detail & Related papers (2023-07-05T20:16:20Z) - UniPoll: A Unified Social Media Poll Generation Framework via
Multi-Objective Optimization [2.9282273207233693]
This article explores the automatic generation of a poll from a social media post by leveraging cutting-edge natural language generation techniques.
We propose a novel unified poll generation framework called UniPoll.
It employs prompt tuning with multi-objective optimization to bolster the connection exploration between contexts (posts and comments) and polls (questions and answers)
arXiv Detail & Related papers (2023-06-12T03:54:04Z) - A Comprehensive Review of Visual-Textual Sentiment Analysis from Social
Media Networks [2.048226951354646]
Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions.
The analysis of these feelings revealed various applications, including brand evaluations, YouTube film reviews and healthcare applications.
Our study focuses on the field of multimodal sentiment analysis, which examines visual and textual data posted on social media networks.
arXiv Detail & Related papers (2022-07-05T16:28:47Z) - iFacetSum: Coreference-based Interactive Faceted Summarization for
Multi-Document Exploration [63.272359227081836]
iFacetSum integrates interactive summarization together with faceted search.
Fine-grained facets are automatically produced based on cross-document coreference pipelines.
arXiv Detail & Related papers (2021-09-23T20:01:11Z) - A survey on extremism analysis using Natural Language Processing [7.885207996427683]
This survey aims to review the contributions of NLP to the field of extremism research.
The content includes a description and comparison of the frequently used NLP techniques, how they were applied and the insights they provided.
future trends, challenges and directions derived from these highlights are suggested.
arXiv Detail & Related papers (2021-03-28T11:05:43Z) - Named Entity Recognition for Social Media Texts with Semantic
Augmentation [70.44281443975554]
Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts.
We propose a neural-based approach to NER for social media texts where both local (from running text) and augmented semantics are taken into account.
arXiv Detail & Related papers (2020-10-29T10:06:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.