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الرأي المالي التعدين
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في هذا البرنامج التعليمي، سنظهر أين نحن وأين سنكون في هؤلاء الباحثين المهتمين بهذا الموضوع.نقسم هذا البرنامج التعليمي في ثلاثة أجزاء، بما في ذلك تعدين الرأي المالي الخشبي، والتعدين الرأي المالي المحتلة الجميلة، والاتجاهات البحثية المحتملة.يبدأ هذا البرنامج التعليمي بتقديم المكونات في الرأي المالي المقترح في جدول أعمالنا ويلخص دراساتها ذات الصلة.كما نسلط الضوء على مهمة تعني آراء العملاء تجاه الخدمات المالية في صناعة الاصطناعي، ومقارنتها بالآراء المعتادة.سيتم تناول العديد من الأسئلة البحوث المحتملة.نأمل أن تكتسب جمهور هذا البرنامج التعليمي لمحة عامة عن التعدين في الرأي المالي ومعرفة اتجاهات البحث الخاصة بهم.
In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into three parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and possible research directions. This tutorial starts by introducing the components in a financial opinion proposed in our research agenda and summarizes their related studies. We also highlight the task of mining customers' opinions toward financial services in the FinTech industry, and compare them with usual opinions. Several potential research questions will be addressed. We hope the audiences of this tutorial will gain an overview of financial opinion mining and figure out their research directions.
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This paper presents multidimensional Social Opinion Mining on user-generated content gathered from newswires and social networking services in three different languages: English ---a high-resourced language, Maltese ---a low-resourced language, and M
altese-English ---a code-switched language. Multiple fine-tuned neural classification language models which cater for the i) English, Maltese and Maltese-English languages as well as ii) five different social opinion dimensions, namely subjectivity, sentiment polarity, emotion, irony and sarcasm, are presented. Results per classification model for each social opinion dimension are discussed.
The amount of information available online can be overwhelming for users to digest, specially when dealing with other users' comments when making a decision about buying a product or service. In this context, opinion summarization systems are of grea
t value, extracting important information from the texts and presenting them to the user in a more understandable manner. It is also known that the usage of semantic representations can benefit the quality of the generated summaries. This paper aims at developing opinion summarization methods based on Abstract Meaning Representation of texts in the Brazilian Portuguese language. Four different methods have been investigated, alongside some literature approaches. The results show that a Machine Learning-based method produced summaries of higher quality, outperforming other literature techniques on manually constructed semantic graphs. We also show that using parsed graphs over manually annotated ones harmed the output. Finally, an analysis of how important different types of information are for the summarization process suggests that using Sentiment Analysis features did not improve summary quality.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media
such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election
Following the increasing performance of neural machine translation systems, the paradigm of using automatically translated data for cross-lingual adaptation is now studied in several applicative domains. The capacity to accurately project annotations
remains however an issue for sequence tagging tasks where annotation must be projected with correct spans. Additionally, when the task implies noisy user-generated text, the quality of translation and annotation projection can be affected. In this paper we propose to tackle multilingual sequence tagging with a new span alignment method and apply it to opinion target extraction from customer reviews. We show that provided suitable heuristics, translated data with automatic span-level annotation projection can yield improvements both for cross-lingual adaptation compared to zero-shot transfer, and data augmentation compared to a multilingual baseline.
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicabilit
y to opinion texts. We address this gap in the literature, studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of opinion similarity'' but not opinion difference''. Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence-embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.
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