Academic literature on the topic 'Text analysis'
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Journal articles on the topic "Text analysis"
Z.K., Orazimbetova, and Mukhiyatdinova T. "LINGUISTIC ANALYSIS OF NEWSPAPER TEXT." CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES 02, no. 10 (October 1, 2021): 82–85. http://dx.doi.org/10.37547/philological-crjps-02-10-16.
Full textNeha K, Shah. "Introduction of Text mining and an Analysis of Text mining Techniques." Paripex - Indian Journal Of Research 2, no. 2 (January 15, 2012): 56–57. http://dx.doi.org/10.15373/22501991/feb2013/18.
Full textV., Dr Sellam. "Text Analysis Via Composite Feature Extraction." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 310–20. http://dx.doi.org/10.5373/jardcs/v12i4/20201445.
Full textH., D. P. "Text analysis." Nature 356, no. 6372 (April 1992): 740. http://dx.doi.org/10.1038/356740a0.
Full textD., Mhamdi. "Job Recommendation System based on Text Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1025–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201575.
Full textKhan, Nida Zafar, and Prof S. R. Yadav. "Analysis of Text Classification Algorithms: A Review." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 579–81. http://dx.doi.org/10.31142/ijtsrd21448.
Full textAdewumi, Sunday Eric. "Character Analysis Scheme for Compressing Text Files." International Journal of Computer Theory and Engineering 7, no. 5 (October 2015): 362–65. http://dx.doi.org/10.7763/ijcte.2015.v7.986.
Full textMakhmudovna, Madirimova Sokhiba, Abdulhaq Rahimi Saripul, and Jamila Eisar. "ANALYSIS OF TEXT DIFFERENCES IN MUTRIB'S WORKS." American Journal Of Philological Sciences 03, no. 04 (April 1, 2023): 41–47. http://dx.doi.org/10.37547/ajps/volume03issue04-07.
Full textDing, Yong, Yongsheng Han, Guozhen Lu, and Xinfeng Wu. "Boundedness of Singular Integrals on Multiparameter Weighted Hardy Spaces $\text{{\textit{H}}}^\text{{\textit{p}}}_{\text{{\textit{w}}}}\ (\mathbb{R}^{\text{{\textit{n}}}}\times \mathbb{R}^{\text{{\textit{m}}}})$." Potential Analysis 37, no. 1 (August 9, 2011): 31–56. http://dx.doi.org/10.1007/s11118-011-9244-y.
Full textFréchet, Nadjim, Justin Savoie, and Yannick Dufresne. "Analysis of Text-Analysis Syllabi: Building a Text-Analysis Syllabus Using Scaling." PS: Political Science & Politics 53, no. 2 (November 29, 2019): 338–43. http://dx.doi.org/10.1017/s1049096519001732.
Full textDissertations / Theses on the topic "Text analysis"
Haggren, Hugo. "Text Similarity Analysis for Test Suite Minimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290239.
Full textMjukvarutestning är den mest kostsamma fasen inom mjukvaruutveckling. Därför är det förståeligt varför testoptimering är ett kritiskt område inom mjukvarubranschen. Inom mjukvarutestning ställer den gradvisa ökningen av testfall stora krav på testresurser (budget och tid). Test Suite Minimization anses vara ett potentiellt tillvägagångssätt för att hantera problemet med växande testsamlingar. Flera minimiseringsmetoder har föreslagits för att effektivt hantera testsamlingars storleksproblem. Att föreslå en bra lösning för minimering av antal testfall är en utmanande uppgift, där flera parametrar som kodtäckning, kravtäckning och testkostnad måste övervägas innan man tar bort ett testfall från testcykeln. Denna uppsats föreslår och utvärderar två olika NLP-baserade metoder för likhetsanalys mellan testfall för manuell integration, som kan användas för minimering av testsamlingar. Den ena metoden baseras på syntaktisk textlikhetsanalys, medan den andra är en maskininlärningsbaserad semantisk strategi. Genomförbarheten av de föreslagna lösningarna studeras genom analys av industriella användningsfall hos Ericsson AB i Sverige. Resultaten visar att den semantiska metoden knappt lyckas överträffa den syntaktiska metoden. Medan båda tillvägagångssätten visar lovande resultat, måste efterföljande studier göras för att ytterligare utvärdera den semantiska likhetsbaserade metoden.
Romsdorfer, Harald. "Polyglot text to speech synthesis text analysis & prosody control." Aachen Shaker, 2009. http://d-nb.info/993448836/04.
Full textKay, Roderick Neil. "Text analysis, summarising and retrieval." Thesis, University of Salford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360435.
Full textHaselton, Curt B. Deierlein Gregory G. "Assessing seismic collapse safety of modern reinforced concrete moment-frame buildings." Berkeley, Calif. : Pacific Earthquake Engineering Research Center, 2008. http://nisee.berkeley.edu/elibrary/Text/200803261.
Full textOzsoy, Makbule Gulcin. "Text Summarization Using Latent Semantic Analysis." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612988/index.pdf.
Full textO'Connor, Brendan T. "Statistical Text Analysis for Social Science." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/541.
Full textLin, Yuhao. "Text Analysis in Fashion : Keyphrase Extraction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290158.
Full textFörmågan att extrahera användbar information från texter och presentera den i form av strukturerade attribut är ett viktigt steg mot att göra produktjämförelsesalgoritmen på ett smartare och bättre sätt. Vissa tidigare arbeten utnyttjar statistiska funktioner som ordfrekvens och grafmodeller för att förutsäga nyckelfraser. Under de senaste åren har djupa neurala nätverk visat sig vara de senaste metoderna för att hantera språkmodellering. Framgångsrika exempel inkluderar Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional Encoder Representations from Transformers (BERT) och deras variationer. Dessutom kan vissa ordinbäddningstekniker som word2vec[1] också vara till hjälp för att förbättra prestandan. Förutom dessa tekniker är en datauppsättning av hög kvalitet också viktig för modellernas effektivitet. I detta projekt strävar vi efter att utveckla pålitliga och effektiva maskininlärningsmodeller för utvinning av nyckelfraser. På Norna AB har vi en samling produktbeskrivningar från olika leverantörer utan nyckelfrasnoteringar, vilket motiverar användningen av metoder utan tillsyn. De bör kunna extrahera användbara nyckelfraser som fångar funktionerna i en produkt. För att ytterligare utforska kraften i djupa neurala nätverk implementerar vi också flera modeller för djupinlärning. Datasetet har två delar, den första delen kallas modedataset där nyckelfraser extraheras med vår metod utan tillsyn. Den andra delen är en offentlig dataset i nyhetsdomänen. Vi finner att deep learning-modeller också kan extrahera meningsfulla nyckelfraser och överträffa den oövervakade modellen. Precision, återkallning och F1-poäng används som utvärderingsmått. Resultatet visar att modellen som använder LSTM och CRF uppnår optimal prestanda. Vi jämför också prestanda för olika modeller med avseende på keyphrase längder och nyckelfras nummer. Resultatet indikerar att alla modeller presterar bättre på att förutsäga korta tangentfraser. Vi visar också att vår raffinerade modell har fördelen att förutsäga långa tangentfraser, vilket är utmanande inom detta område.
Maisto, Alessandro. "A Hybrid Framework for Text Analysis." Doctoral thesis, Universita degli studi di Salerno, 2017. http://hdl.handle.net/10556/2481.
Full textIn Computational Linguistics there is an essential dichotomy between Linguists and Computer Scientists. The rst ones, with a strong knowledge of language structures, have not engineering skills. The second ones, contrariwise, expert in computer and mathematics skills, do not assign values to basic mechanisms and structures of language. Moreover, this discrepancy, especially in the last decades, has increased due to the growth of computational resources and to the gradual computerization of the world; the use of Machine Learning technologies in Arti cial Intelligence problems solving, which allows for example the machines to learn , starting from manually generated examples, has been more and more often used in Computational Linguistics in order to overcome the obstacle represented by language structures and its formal representation. The dichotomy has resulted in the birth of two main approaches to Computational Linguistics that respectively prefers: rule-based methods, that try to imitate the way in which man uses and understands the language, reproducing syntactic structures on which the understanding process is based on, building lexical resources as electronic dictionaries, taxonomies or ontologies; statistic-based methods that, conversely, treat language as a group of elements, quantifying words in a mathematical way and trying to extract information without identifying syntactic structures or, in some algorithms, trying to confer to the machine the ability to learn these structures. One of the main problems is the lack of communication between these two di erent approaches, due to substantial di erences characterizing them: on the one hand there is a strong focus on how language works and on language characteristics, there is a tendency to analytical and manual work. From other hand, engineering perspective nds in language an obstacle, and recognizes in the algorithms the fastest way to overcome this problem. However, the lack of communication is not only an incompatibility: following Harris, the best way to approach natural language, could result by taking the best of both. At the moment, there is a large number of open-source tools that perform text analysis and Natural Language Processing. A great part of these tools are based on statistical models and consist on separated modules which could be combined in order to create a pipeline for the processing of the text. Many of these resources consist in code packages which have not a GUI (Graphical User Interface) and they result impossible to use for users without programming skills. Furthermore, the vast majority of these open-source tools support only English language and, when Italian language is included, the performances of the tools decrease signi cantly. On the other hand, open source tools for Italian language are very few. In this work we want to ll this gap by present a new hybrid framework for the analysis of Italian texts. It must not be intended as a commercial tool, but the purpose for which it was built is to help linguists and other scholars to perform rapid text analysis and to produce linguistic data. The framework, that performs both statistical and rule-based analysis, is called LG-Starship. The idea is to built a modular software that includes, in the beginning, the basic algorithms to perform di erent kind of analysis. Modules will perform the following tasks: Preprocessing Module: a module with which it is possible to charge a text, normalize it or delete stop-words. As output, the module presents the list of tokens and letters which compose the texts with respective occurrences count and the processed text. Mr. Ling Module: a module with which POS tagging and Lemmatization are performed. The module also returns the table of lemmas with the count of occurrences and the table with the quanti cation of grammatical tags. Statistic Module: with which it is possible to calculate Term Frequency and TF-IDF of tokens or lemmas, extract bi-grams and tri-grams units and export results as tables. Semantic Module: which use The Hyperspace Analogue to Language algorithm to calculate semantic similarity between words. The module returns similarity matrices of words per word which can be exported and analyzed. SyntacticModule: which analyze syntax structures of a selected sentence and tag the verbs and its arguments with semantic labels. The objective of the Framework is to build an all-in-one platform for NLP which allows any kind of users to perform basic and advanced text analysis. With the purpose of make the Framework accessible to users who have not speci c computer science and programming language skills, the modules have been provided with an intuitive GUI. The framework can be considered hybrid in a double sense: as explained in the previous lines, it uses both statistical and rule/based methods, by relying on standard statistical algorithms or techniques, and, at the same time, on Lexicon-Grammar syntactic theory. In addition, it has been written in both Java and Python programming languages. LG-Starship Framework has a simple Graphic User Interface but will be also released as separated modules which may be included in any NLP pipelines independently. There are many resources of this kind, but the large majority works for English. There are very few free resources for Italian language and this work tries to cover this need by proposing a tool which can be used both by linguists or other scientist interested in language and text analysis who have no idea about programming languages, as by computer scientists, who can use free modules in their own code or in combination with di erent NLP algorithms. The Framework takes the start from a text or corpus written directly by the user or charged from an external resource. The LG-Starship Framework work ow is described in the owchart shown in g. 1. The pipeline shows that the Pre-Processing Module is applied on original imported or generated text in order to produce a clean and normalized preprocessed text. This module includes a function for text splitting, a stop-word list and a tokenization method. On the text preprocessed the Statistic Module or the Mr. Ling Module can be applied. The rst one, which includes basic statistics algorithm as Term Frequency, tf-idf and n-grams extraction, produces as output databases of lexical and numerical data which can be used to produce charts or perform more external analysis; the second one, is divided in two main task: a Pos tagger, based on the Averaged Perceptron Tagger [?] and trained on the Paisà Corpus [Lyding et al., 2014], perform the Part-Of- Speech Tagging and produce an annotated text. A lemmatization method, which relies on a set of electronic dictionaries developed at the University of Salerno [Elia, 1995, Elia et al., 2010], take as input the Postagged text and produces a new lemmatized version of original text with information about syntactic and semantic properties. This lemmatized text, which can also be processed with the Statistic Module, serves as input for two deeper level of text analysis carried out by both the Syntactic Module and the Semantic Module. The rst one lays on the Lexicon Grammar Theory [Gross, 1971, 1975] and use a database of Predicate structures in development at the Department of Political, Social and Communication Science. Its objective is to produce a Dependency Graph of the sentences that compose the text. The Semantic Module uses the Hyperspace Analogue to Language distributional semantics algorithm [Lund and Burgess, 1996] trained on the Paisà Corpus to produce a semantic network of the words of the text. These work ow has been included in two di erent experiments in which two User Generated Corpora have been involved. The rst experiment represent a statistical study of the language of Rap Music in Italy through the analysis of a great corpus of Rap Song lyrics downloaded from on line databases of user generated lyrics. The second experiment is a Feature-Based Sentiment Analysis project performed on user product reviews. For this project we integrated a large domain database of linguistic resources for Sentiment Analysis, developed in the past years by the Department of Political, Social and Communication Science of the University of Salerno, which consists of polarized dictionaries of Verbs, Adjectives, Adverbs and Nouns. These two experiment underline how the linguistic framework can be applied to di erent level of analysis and to produce both Qualitative data and Quantitative data. For what concern the obtained results, the Framework, which is only at a Beta Version, obtain discrete results both in terms of processing time that in terms of precision. Nevertheless, the work is far from being considered complete. More algorithms will be added to the Statistic Module and the Syntactic Module will be completed. The GUI will be improved and made more attractive and modern and, in addiction, an open-source on-line version of the modules will be published. [edited by author]
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Algarni, Abdulmohsen. "Relevance feature discovery for text analysis." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48230/1/Abdulmohsen_Algarni_Thesis.pdf.
Full textRomsdorfer, Harald [Verfasser]. "Polyglot Text-to-Speech Synthesis : Text Analysis & Prosody Control / Harald Romsdorfer." Aachen : Shaker, 2009. http://d-nb.info/1156517354/34.
Full textBooks on the topic "Text analysis"
Wachsmuth, Henning. Text Analysis Pipelines. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25741-9.
Full textWildfeuer, Janina. Film Text Analysis. New York: Routledge, [2016] | Series: Routledge advances in: Routledge, 2016. http://dx.doi.org/10.4324/9781315692746.
Full textEmel, Sözer, ed. Text connexity, text coherence: Aspects, methods, results. Hamburg: H. Buske, 1985.
Find full textJockers, Matthew L., and Rosamond Thalken. Text Analysis with R. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5.
Full textPopping, R. Computer-assisted text analysis. London: Sage Publications, 2000.
Find full textText and discourse analysis. London: Routledge, 1995.
Find full textIntroducing electronic text analysis. New York: Routledge, 2006.
Find full textCobham, David. Macroeconomic analysis: Anintermediate text. London: Longman, 1987.
Find full textMichel, Charolles, Petöfi János S, and Sözer Emel, eds. Research in text connexity and text coherence: A survey. Hamburg: H. Buske, 1986.
Find full text1963-, Warnke Ingo, ed. Schnittstelle Text: Diskurs. Frankfurt am Main: Peter Lang, 1999.
Find full textBook chapters on the topic "Text analysis"
Bainbridge, William Sims. "Text Analysis." In Human–Computer Interaction Series, 151–76. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5604-8_7.
Full textArnold, Taylor, and Lauren Tilton. "Text Analysis." In Quantitative Methods in the Humanities and Social Sciences, 157–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20702-5_10.
Full textPetrocchi, Alessandra. "Text analysis." In The Gaṇitatilaka and its Commentary, 285–417. Abingdon, Oxon ; New York, NY : Routledge, 2019. |: Routledge, 2019. http://dx.doi.org/10.4324/9781351022262-5.
Full textDengah, H. J. François, Jeffrey G. Snodgrass, Evan R. Polzer, and William Cody Nixon. "Text analysis." In Systematic Methods for Analyzing Culture, 65–82. New York : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9781003092179-7.
Full textLeslie, Larry Z. "Text Analysis." In Communication Research Methods in Postmodern Culture, 145–71. Second edition. | New York, NY : Routledge, 2017. |: Routledge, 2017. http://dx.doi.org/10.4324/9781315231730-10.
Full textWang, Wei. "Text analysis." In The Routledge Handbook of Research Methods in Applied Linguistics, 453–63. New York : Taylor and Francis, 2020. | Series: Routledge handbooks in applied linguistics: Routledge, 2019. http://dx.doi.org/10.4324/9780367824471-38.
Full textWorch, Thierry, Julien Delarue, Vanessa Rios de Souza, and John Ennis. "Text Analysis." In Data Science for Sensory and Consumer Scientists, 279–300. New York: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003028611-13.
Full textBonnell, Jerry, and Mitsunori Ogihara. "Text Analysis." In Exploring Data Science with R and the Tidyverse, 441–72. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003320845-10.
Full textBeach, David. "Music and Text." In Schenkerian Analysis, 252–90. Second edition. | New York ; London : Routledge, 2019. | Previous edition published under title: Advanced Schenkerian analysis.: Routledge, 2019. http://dx.doi.org/10.4324/9780429453793-10.
Full textWachsmuth, Henning. "Text Analysis Pipelines." In Lecture Notes in Computer Science, 19–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25741-9_2.
Full textConference papers on the topic "Text analysis"
Thi Xuan Lam, Thanh, Anh Duc Le, and Masaki Nakagawa. "User Interface for Text and Non-Text Classification." In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW). IEEE, 2019. http://dx.doi.org/10.1109/icdarw.2019.20044.
Full textXue, Zijun. "Scalable Text Analysis." In WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018661.3022750.
Full textNoronha, Perpetua F., Madhu Bhan, M. Niranjanamurthy, and D. Chandana. "Text Analysis Tool." In 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1). IEEE, 2023. http://dx.doi.org/10.1109/icaia57370.2023.10169652.
Full textWang, Xiufei, Lei Huang, and Changping Liu. "A New Block Partitioned Text Feature for Text Verification." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.61.
Full textZhao, Miao, Rui-Qi Wang, Fei Yin, Xu-Yao Zhang, Lin-Lin Huang, and Jean-Marc Ogier. "Fast Text/non-Text Image Classification with Knowledge Distillation." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00234.
Full textZhu, Xiangyu, Yingying Jiang, Shuli Yang, Xiaobing Wang, Wei Li, Pei Fu, Hua Wang, and Zhenbo Luo. "Deep Residual Text Detection Network for Scene Text." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. http://dx.doi.org/10.1109/icdar.2017.137.
Full textNakamura, Toshiki Nakamura, Anna Zhu, and Seiichi Uchida. "Scene Text Magnifier." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00137.
Full textRomero, Veronica, Joan Andreu Sanchez, Vicente Bosch, Katrien Depuydt, and Jesse de Does. "Influence of text line segmentation in Handwritten Text Recognition." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333819.
Full textZhang, Chengquan, Cong Yao, Baoguang Shi, and Xiang Bai. "Automatic discrimination of text and non-text natural images." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333889.
Full textNicolaou, Anguelos, and Basilis Gatos. "Handwritten Text Line Segmentation by Shredding Text into its Lines." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.243.
Full textReports on the topic "Text analysis"
Spirling, Arthur. Text Analysis: Text as Data with R. Instats Inc., 2022. http://dx.doi.org/10.61700/a52fcasdqm1du469.
Full textSpirling, Arthur. Text Analysis: Text as Data with R. Instats Inc., 2022. http://dx.doi.org/10.61700/lolq2hyg9sn6d469.
Full textStevenson, Mark. Individual Profiling Using Text Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 2016. http://dx.doi.org/10.21236/ad1009417.
Full textMontiel Olea, César E., and Leonardo R. Corral. Text Analysis of Project Completion Reports. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003611.
Full textBock, Geoffrey. Meta Tagging and Text Analysis from ClearForest. Boston, MA: Patricia Seybold Group, February 2002. http://dx.doi.org/10.1571/pr2-21-02cc.
Full textSchryver, Jack C., Edmon Begoli, Ajith Jose, and Christopher Griffin. Inferring Group Processes from Computer-Mediated Affective Text Analysis. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1004442.
Full textBengston, David N. Applications of computer-aided text analysis in natural resources. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station, 2000. http://dx.doi.org/10.2737/nc-gtr-211.
Full textGiorcelli, Michela, Nicola Lacetera, and Astrid Marinoni. Does Scientific Progress Affect Culture? A Digital Text Analysis. Cambridge, MA: National Bureau of Economic Research, January 2019. http://dx.doi.org/10.3386/w25429.
Full textFischer, Eric, Rebecca McCaughrin, Saketh Prazad, and Mark Vandergon. Fed Transparency and Policy Expectation Errors: A Text Analysis Approach. Federal Reserve Bank of New York, November 2023. http://dx.doi.org/10.59576/sr.1081.
Full textHan, Xuehua, Juanle Wang, and Yuelei Yuan. Extraction and Analysis of Earthquake Events Information based on Web Text. International Science Council, 2019. http://dx.doi.org/10.24948/2019.06.
Full text