Opinion mining, specifically in the investment sector, has experienced a significant increase in interest over recent years. This paper presents a novel approach to overcome current limitations in assessing and ranking investor opinions based on profitability. The study introduces a pre-finetuning scheme to improve language models' capacity to distinguish professionalism, thus enabling ranking of all available opinions. Furthermore, the paper evaluates ranking results using traditional metrics and suggests the use of a pairwise setting for better performances over a regression setting. Lastly, our method is shown to be effective across various investor opinion tasks, encompassing both professional and amateur investors. The results indicate that this approach significantly enhances the efficiency and accuracy of opinion mining in the investment sector.

Numerous effective methods have been developed to improve query-focused summarization (QFS) performance, e.g., pre-trained model-based and query-answer relevance-based methods. However, these methods still suffer from missing or redundant information due to the inability to capture and effectively utilize the interrelationship between the query and the source document, as well as between the source document and its generated summary, resulting in the summary being unable to answer the query or containing additional unrequired information. To mitigate this problem, we propose an end-to-end hierarchical two-stage summarization model, that first predicts essential content, and then generates a summary by emphasizing the predicted important sentences while maintaining separate encodings for the query and the source, so that it can comprehend not only the query itself but also the essential information in the source. We evaluated the proposed model on two QFS datasets, and the results indicated its overall effectiveness and that of each component.

In this article, we provide an overview of ACM TWEB’s special issue, Financial Technology on the Web . This special issue covers diverse topics: (1) a new architecture for leveraging online news to investment and risk management, (2) a cross-platform analysis of the post quality and users’ behaviors, and (3) an empirical study on disentangling decentralized finance compositions. In addition to a guide for the special issue, we also share a brief opinion on the future of financial technology on the Web.

Social media has become a platform where users post their messages about a wide range of topics, making it a useful source of information to summarize events such as sports matches. Previous summaries of sports matches generated using social media tended to be biased towards one of the teams, due to a high proportion of the posts used being from fans of the teams involved. This is problematic because in general people desire summaries that are free from bias and objective. To remedy this problem and generate higher quality summaries, we propose two approaches. The first is a function maximization method which measures the objectivity of each post based on its constituent words. The second is a neural network-based approach, where we use an encoder-decoder architecture. Then, we compare them with an existing approach and show promising results that indicate the effectiveness of our methods.

Small images of emojis have unique characteristics as additional information in understanding writers’ intentions. They enable social media users to emphasize their emotions and to express gestural movements in their posts. In addition to emojis, kaomojis (emoticons or facemarks) also behave in a similar way. They are composed of a sequence of characters, which are popularized especially in Asian countries. Although both emojis and kaomojis fulfill similar functions and share the same meaning that can be clues in opinion mining or sentiment analysis, the previous researches have been biased to explore emojis and kaomojis separately. In this paper, we align emojis and kaomojis together as a single token in the Japanese context to offer a bridge between them. Specifically, we aim to judge whether emojis and kaomojis share the same meaning or are similar with each other. We assume that emojis and kaomojis are both a single word in order to obtain their linguistic information with the skip-gram model. Furthermore, we present a new approach to consider the appearances of emojis and kaomojis in themselves, meaning that we explore the information of their visually similar shapes. We regard both of them as a single image to take into account their visual information with the CNN model. We merge two different perspectives toward emojis and kaomojis by exploring their linguistic and visual information simultaneously on the same space. The experimental results showed that we can align an unlimited number of emojis and kaomojis together with their representations (embeddings), and adding the visual information to the linguistic information can improve their representations.

We present an automated, real-time tweet selection system for TV news programs. The system collects tweets related to a TV news program and chooses an appropriate tweet every 10 seconds for display. The selection procedure consists of two steps: assessing the importance of each tweet, and assessing the difference between the tweet and previously selected tweets. The previously selected tweets are taken into consideration for the purpose of maintaining the diversity of the entire set of selected tweets. We conducted experiments with a real TV news program and showed that the system developed in this study can select appropriate tweets.

Twitter has emerged as a platform for crowds to express their opinions. Many Twitter users post their opinions, impressions, and statuses of televised events such as sports events. However, since the volume of such posts is extremely huge, it requires a lot of time and effort to understand what happens within events. We propose a method of generating live sports updates from Twitter posts on an event. Our method selects descriptive and prompt tweets that are posted within a short time after important sub events by exploiting users called good reporters, who promptly explain what is happening at each moment throughout the event. The experimental results indicated that our new technique generated more comprehensive updates than other methods presented in previous work.

We propose a new model for the guided text summarization task. In this task, it is required that a generated summary covers all the aspects, which are predefined for the topic of the given document cluster; for example, aspects for the topic "Accidents and Natural Disasters" include WHAT, WHEN, WHERE, WHY, WHO AFFECTED, DAMAGES and COUNTERMEASURES. We use as a scorer for an aspect, the maximum entropy classifier that predicts whether each sentence reflects the aspect or not. We formalize the coverage of the aspects as a max-min problem, which enables a summary to cover aspects in a well-balanced manner. In the max-min problem, the minimum of the aspect scores is going to be maximized so that the summary contains all the aspects as much as possible. Furthermore, we integrate the model based on the max-min problem with the maximum coverage summarization model, which generates a summary containing as many conceptual units as possible. Through the experiments on benchmark datasets for the guided summarization, we show that our model outperforms other approaches in terms of ROUGE-2.

We propose to use a structured output learning for summary generation based on the maximum coverage problem. Our method learns a function that outputs the benefit of each conceptual unit in the document cluster for this summarization model. In the training, we iteratively run a greedy algorithm that accepts items (sentences) with different costs (length) in order to generate a summary within the given maximum length limit. We empirically show that the structured output learning works well for this task and also examine its behavior in several dierent settings.

We propose a multi-document generic summarization model based on the budgeted median problem. Our model selects sentences to generate a summary so that every sentence in the document cluster can be assigned to and be represented by a sentence in the summary as much as possible. The advantage of this model is that it covers the entire relevant part of the document cluster through sentence assignment and can incorporate asymmetric relations between sentences such as textual entailment.

Among a huge number of blogs on the internet, only some of them are considered to have great contents and worth to be explored. We call such kind of blogs cool blogs and attempt to identify them. To solve the cool blog identification problem, we consider three assumptions on cool blogs: (1) cool blogs tend to have definite topics, (2) cool blogs tend to have sufficient amount of blog entries, and (3) cool blogs tend to have certain levels of topic consistency among their blog entries. Corresponding to these assumptions, we extract a mixture of topic probabilities using a topic model, exploit the number of blog entries of each blog, and calculate the topic consistency among blog entries using distance functions over topic probabilities, respectively. We show the benefits of the proposed assumptions through these features. A feature unification model is also presented to achieve highest effectiveness. The experimental results on Japanese blog data show that we can improve the classification results by applying proposed assumptions.