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.

基于社交媒体数据的无监督短语摘要生成方法表现出显著的优势，并可用于提取外部洞察以帮助金融科技组织更好地管理其产品、优惠和推广活动。

金融科技组织的关键短语增强无监督摘要生成

Key-phrase boosted unsupervised summary generation for FinTech  organization

Text classification is a popular topic of natural language processing, which
has currently attracted numerous research efforts worldwide. The significant
increase of data in social media requires the vast attention of researchers to
analyze such data. There are various studies in this field in many languages
but limited to the Vietnamese language. Therefore, this study aims to classify
Vietnamese texts on social media from three different Vietnamese benchmark
datasets. Advanced deep learning models are used and optimized in this study,
including CNN, LSTM, and their variants. We also implement the BERT, which has
never been applied to the datasets. Our experiments find a suitable model for
classification tasks on each specific dataset. To take advantage of single
models, we propose an ensemble model, combining the highest-performance models.
Our single models reach positive results on each dataset. Moreover, our
ensemble model achieves the best performance on all three datasets. We reach
86.96% of F1- score for the HSD-VLSP dataset, 65.79% of F1-score for the
UIT-VSMEC dataset, 92.79% and 89.70% for sentiments and topics on the UIT-VSFC
dataset, respectively. Therefore, our models achieve better performances as
compared to previous studies on these datasets.

使用深度学习模型和集成模型对社交媒体上的越南文本进行分类，实现了比之前研究更好的性能。