FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2205717503> ?p ?o ?g. }

• W2205717503 abstract "Information Explosion has caused the demand for customized text-mining in every imaginable area to sky-rocket. Text mining is needed in many areas, a few of which are: search engine development, spam-filtering and text-summarization. Every context requires its own customized text mining algorithms in order to achieve best results. The specific context of this research is market prediction for the foreign exchange market. The objective is to utilize news-headlines to predict market-movements 1 to 3 hours after news release.The literature on recent research efforts in behavioral economics confirms that investors’ aggregate behavioral reactions to information released in the news can drive prices up or down. This theoretical basis constitutes the economic foundation of this investigation.After economic comprehension of the problem at hand; available systems in the literature which operate in a comparable context are reviewed. The major finding of this review is that context-specific text mining algorithms are lacking. The main underlying text-mining challenge that seems to deserve immediate attention is the sparse and high dimensional nature of the feature-space.Therefore, this work produces a multi-layer dimension reduction algorithm to respond to this need.The algorithm tackles a different root cause of the problem at each layer. The first layer is termed the Semantic Abstraction Layer and addresses the problem of co-reference in text mining that is contributing to sparsity. Co-reference occurs when two or more words in a text corpus refer to the same concept. This work produces a custom approach by the name of Heuristic-Hypernyms Modeling which creates a way to recognize words with the same parent-word to be regarded as one entity. As a result, prediction accuracy increases significantly at this layer which is attributed to appropriate noise-reduction from the feature-space.The second layer is termed Sentiment Integration Layer, which integrates sentiment analysis capability into the algorithm by proposing a sentiment weight by the name of SumScore that reflects investors’ sentiment. This layer reduces the dimensions by eliminating those that are of zero value in terms of sentiment and thereby improves prediction accuracy.The third layer encompasses a dynamic model creation algorithm, termed Synchronous Targeted Feature Reduction (STFR). It is suitable for the challenge at hand whereby the mining of a stream of text is concerned. It updates the models with the most recent information available and, more importantly, it ensures that the dimensions are reduced to a number that is many times smaller.The algorithm and each of its layers are extensively evaluated using real market data and news content across multiple years and have proven to be solid and superior to any other comparable solution. On top of a well-rounded multifaceted algorithm, this work contributes a much needed research framework for this context with a test-bed of data that must make future research endeavors more convenient. The produced algorithm is scalable and its modular design allows improvement in each of its layers in future research." @default.
• W2205717503 created "2016-06-24" @default.
• W2205717503 creator A5036057749 @default.
• W2205717503 date "2015-01-01" @default.
• W2205717503 modified "2023-09-24" @default.
• W2205717503 title "A multi-layer dimension reduction algorithm for text mining of news in forex / Arman Khadjeh Nassirtoussi" @default.
• W2205717503 hasPublicationYear "2015" @default.
• W2205717503 type Work @default.
• W2205717503 sameAs 2205717503 @default.
• W2205717503 citedByCount "0" @default.
• W2205717503 crossrefType "dissertation" @default.
• W2205717503 hasAuthorship W2205717503A5036057749 @default.
• W2205717503 hasConcept C111472728 @default.
• W2205717503 hasConcept C119857082 @default.
• W2205717503 hasConcept C124101348 @default.
• W2205717503 hasConcept C124304363 @default.
• W2205717503 hasConcept C136764020 @default.
• W2205717503 hasConcept C138885662 @default.
• W2205717503 hasConcept C154945302 @default.
• W2205717503 hasConcept C166957645 @default.
• W2205717503 hasConcept C170858558 @default.
• W2205717503 hasConcept C176775163 @default.
• W2205717503 hasConcept C197046077 @default.
• W2205717503 hasConcept C205649164 @default.
• W2205717503 hasConcept C23123220 @default.
• W2205717503 hasConcept C2522767166 @default.
• W2205717503 hasConcept C2779343474 @default.
• W2205717503 hasConcept C35578498 @default.
• W2205717503 hasConcept C41008148 @default.
• W2205717503 hasConcept C71472368 @default.
• W2205717503 hasConceptScore W2205717503C111472728 @default.
• W2205717503 hasConceptScore W2205717503C119857082 @default.
• W2205717503 hasConceptScore W2205717503C124101348 @default.
• W2205717503 hasConceptScore W2205717503C124304363 @default.
• W2205717503 hasConceptScore W2205717503C136764020 @default.
• W2205717503 hasConceptScore W2205717503C138885662 @default.
• W2205717503 hasConceptScore W2205717503C154945302 @default.
• W2205717503 hasConceptScore W2205717503C166957645 @default.
• W2205717503 hasConceptScore W2205717503C170858558 @default.
• W2205717503 hasConceptScore W2205717503C176775163 @default.
• W2205717503 hasConceptScore W2205717503C197046077 @default.
• W2205717503 hasConceptScore W2205717503C205649164 @default.
• W2205717503 hasConceptScore W2205717503C23123220 @default.
• W2205717503 hasConceptScore W2205717503C2522767166 @default.
• W2205717503 hasConceptScore W2205717503C2779343474 @default.
• W2205717503 hasConceptScore W2205717503C35578498 @default.
• W2205717503 hasConceptScore W2205717503C41008148 @default.
• W2205717503 hasConceptScore W2205717503C71472368 @default.
• W2205717503 hasLocation W22057175031 @default.
• W2205717503 hasOpenAccess W2205717503 @default.
• W2205717503 hasPrimaryLocation W22057175031 @default.
• W2205717503 hasRelatedWork W128311924 @default.
• W2205717503 hasRelatedWork W129636700 @default.
• W2205717503 hasRelatedWork W1505837856 @default.
• W2205717503 hasRelatedWork W1524239016 @default.
• W2205717503 hasRelatedWork W1738409213 @default.
• W2205717503 hasRelatedWork W1936070845 @default.
• W2205717503 hasRelatedWork W2038822667 @default.
• W2205717503 hasRelatedWork W2051999570 @default.
• W2205717503 hasRelatedWork W2092238177 @default.
• W2205717503 hasRelatedWork W2109309764 @default.
• W2205717503 hasRelatedWork W2139163981 @default.
• W2205717503 hasRelatedWork W2186091528 @default.
• W2205717503 hasRelatedWork W2214375279 @default.
• W2205717503 hasRelatedWork W2612393806 @default.
• W2205717503 hasRelatedWork W2621583232 @default.
• W2205717503 hasRelatedWork W2806227939 @default.
• W2205717503 hasRelatedWork W2806381151 @default.
• W2205717503 hasRelatedWork W1494240722 @default.
• W2205717503 hasRelatedWork W2597127351 @default.
• W2205717503 hasRelatedWork W304704178 @default.
• W2205717503 isParatext "false" @default.
• W2205717503 isRetracted "false" @default.
• W2205717503 magId "2205717503" @default.
• W2205717503 workType "dissertation" @default.