Kanungsukkasem, Nont

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Kanungsukkasem, Nont
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Kanunasukkasem, Nont
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nont.ka@kmitl.ac.th

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    Finding potential influences of a specific financial market in Twitter
    (2015-01-01)
    Kanungsukkasem, Nont
    ;
    Leelanupab, Teerapong
    This paper proposes a new framework to identify and rank Twitter accounts of which short messages or tweets may influence a specific financial stock price. In this paper, we start by mainly focusing on the first step of our framework, selecting potential influencers based on their association with a particular stock market. With numerous limitations of Twitter service to access and acquire Twitters data, a new methodology is also proposed to use a List feature to select potential financial influencers. It requires only Lists of accounts that contain an official Twitter account of the specific financial market, provided by Twitter's users, through Twitter API. Our methodology is designed to use only a small amount of data, by which it can be practically used under the limitation of Twitter API's crawling rate. Experimental results show that most of the potential influencers returned by our methodology are similar and related to the specific financial markets, which are companies listed on S&P 500 in this experiment. Most of the returned influencers are the official accounts of company or organization from the same sector and news media with a special emphasis on the same industry. Comparing to Twitter's user recommendation service (Who-To-Follow) and a crowdsourcing search for topic experts (Cognos), our methodology returns more related accounts in both percentage and the number.
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    Snapboard: A shared space of visual snippets - A study in individual and asynchronous collaborative web search
    (2015-01-01)
    Leelanupab, Teerapong
    ;
    Kruajirayu, Hannarin
    ;
    Kanungsukkasem, Nont
    People often engage in many search tasks that be collaborative, where two or more individuals work together with the joint information needs. We introduced and built CoZpace, a web-based application that enables a group of users to collaborate on searching the web. We also presented the main feature of CoZpace, named Snapboard, which is a shared board for a collection of group-created visual snippets. The visual snippet is a snapshot of focused and salient information captured by a user. It acts as a visual summarization of web pages, which allows any user to quickly recognize information and to revisit web pages. This paper describes example usage scenarios and initially investigates the ways Snapboard facilitates users in individual and asynchronous collaborative search. We then analyze users’ interactions and discuss how Snapboard supports search collaboration among study participants.
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    Power of crowdsourcing in Twitter to find similar/related users
    (2016-11-18)
    Kanungsukkasem, Nont
    ;
    Leelanupab, Teerapong
    Identifying similar users in social media, e.g., Twitter, is very useful for a variety of applications, such as promoting social connections by user recommendation and defining the type of user accounts for target advertisement. Although many existing methods are available, especially that of Similar-To Framework of Twitter, they require a massive amount of data to process. This has become a major obstacle for non-commercial organizations and in particular academic researchers to study and analyze the similarity, due to the resource requirement to handle large datasets and the limitations of Twitter API to access Tweets per day. Accordingly, this paper proposes a new method that uses only a small amount of data and can be applied by not exceeding Twitter API limitations. Our method, called List Voting, uses only Lists feature that is provided by Twitter to determine accounts of similar users who are likely to produce similar contents. All the Lists are created by Twitter users. Thus, the List can be considered as a crowdsourcing. We also study the characteristics of the definition of crowdsourcing to confirm the consideration. Our experimental result shows that our method gets the benefit of the power of this crowdsourcing and can provide a list of users that are similar or related to a specified user.
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    Recognition of NASDAQ stock symbols in tweets
    (2014-01-01)
    Kanungsukkasem, Nont
    ;
    Netisopakul, Ponrudee
    ;
    Leelanupab, Teerapong
    The massive volume of Twitter data has attracted much attention of researchers to study their correlation with stock market. Tweets with stock symbols can be identified by the prefix with dollar sign or by using some complex techniques. In this paper, we focus on discovering NASDAQ stock symbols in a stream of tweets. We propose a simple but effective methodology to recognize the stock symbols. Stock symbols from NASDAQ company list, WordNet, Wikipedia and sample tweets, as well as a classic method of collocation discovery are employed to filter stock-related tweets. Experimental evaluations show that our methodology outperforms the baseline approach for recognizing NASDAQ stock symbols. © 2013 IEEE.
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    Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction
    (2019-01-01)
    Kanungsukkasem, Nont
    ;
    Leelanupab, Teerapong
    News has been an important source for many financial time series predictions based on fundamental analysis. However, digesting a massive amount of news and data published on the Internet to predict a market can be burdensome. This paper introduces a topic model based on latent Dirichlet allocation (LDA) to discover features from a combination of text, especially news articles and financial time series, denoted as Financial LDA (FinLDA). The features from FinLDA are served as additional input features for any machine learning algorithm to improve the prediction of the financial time series. We provide posterior distributions used in Gibbs sampling for two variants of the FinLDA and propose a framework for applying the FinLDA in a text and data mining for financial time series prediction. The experimental results show that the features from the FinLDA empirically add value to the prediction and give better results than the comparative features including topic distributions from the common LDA.