{"id":3626,"date":"2023-03-15T11:35:05","date_gmt":"2023-03-15T11:35:05","guid":{"rendered":"https:\/\/ceowebltd.com\/blog\/?p=3626"},"modified":"2023-07-07T10:44:45","modified_gmt":"2023-07-07T10:44:45","slug":"ais-electronic-library-aisel-icis-2015-proceedings","status":"publish","type":"post","link":"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/","title":{"rendered":"AIS Electronic Library AISeL ICIS 2015 Proceedings: Sentiment Analysis Meets Semantic Analysis: Constructing Insight Knowledge Bases"},"content":{"rendered":"<p><img decoding=\"async\" class='wp-post-image' style='margin-left:auto;margin-right:auto' src=\"image\/jpeg;base64,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\" width=\"301px\" alt=\"semantics sentiment analysis\" \/><\/p>\n<p><p>Borth et al. [15] proposed to utilize Adjectives Noun Pairs (ANPs), which were explored based on strong co-occurrence relationships with emotion tags of web images. Then, SentiBank [15] and DeepSentiBank [26] were constructed to detect ANPs in the images as semantic feature representations to narrow the gap. The obvious drawback of these methods is that they often treat the problem as a collection of binary classification problems, indicating the presence of visual concepts while ignoring the contextual information.<\/p>\n<\/p>\n<ul>\n<li>If combined with machine learning, semantic analysis lets you dig deeper into your data by making it possible for machines to pull purpose from an unstructured text at scale and in real time.<\/li>\n<li>For example, \u201ccows flow supremely\u201d is grammatically valid (subject\u200a\u2014\u200averb\u200a\u2014\u200aadverb) but it doesn\u2019t make any sense.<\/li>\n<li>It is also an essential component of data science, which involves the collection, analysis, and interpretation of large datasets.<\/li>\n<li>In this post, we\u2019ll look more closely at how Sentiment Analysis works, current models, use cases, the best APIs to use when performing Sentiment Analysis, and current limitations.<\/li>\n<li>Fourth, gamers emphasized that they and enterprises should be equally involved when communicating with each other.<\/li>\n<li>Then, using MS Excel, non-essential columns were removed, including timestamps and user nicknames.<\/li>\n<\/ul>\n<p><p>Concepts and heuristics-based approaches seemed like the best way to implement a solution quickly, but the flexibility of the tool grants a variety of ways. I built this algorithm using expert.ai Studio, which provides access to the core NLU analysis of expert.ai and applies linguistic customization to it in the form of very basic if-then statements. For example, if a specific linguistic condition occurs, then annotate or apply a specific category to text. I decided to use this API to build an NLU-based model anyone could hook up to an RPA or an email automation script. I employed my custom NLP model to classify text and discover the sender\u2019s intention (e.g., <a href=\"https:\/\/www.metadialog.com\/blog\/semantic-analysis-in-nlp\/\">whether the incoming<\/a> message is a complaint, a support request, or a request for information).<\/p>\n<\/p>\n<p><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_73 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Results_and_analysis\" title=\"Results and analysis\">Results and analysis<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Text-based_automatic_personality_prediction_using_KGrAt-Net_a_%E2%80%A6_%E2%80%93_Naturecom\" title=\"Text-based automatic personality prediction using KGrAt-Net: a &#8230; &#8211; Nature.com\">Text-based automatic personality prediction using KGrAt-Net: a &#8230; &#8211; Nature.com<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Sentiment_Analysis_vs_Semantic_Analysis_What_Creates_More_Value\" title=\"Sentiment Analysis vs. Semantic Analysis: What Creates More Value?\">Sentiment Analysis vs. Semantic Analysis: What Creates More Value?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#What_is_semantic_analysis_in_sentiment_analysis\" title=\"What is semantic analysis in sentiment analysis?\">What is semantic analysis in sentiment analysis?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Commercial_Products\" title=\"Commercial Products:\">Commercial Products:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Unveiling_the_Role_of_a_Full-Stack_Data_Scientist_Bridging_the_Gap_Between_Data_and_Insights\" title=\"Unveiling the Role of a Full-Stack Data Scientist: Bridging the Gap Between Data and Insights\">Unveiling the Role of a Full-Stack Data Scientist: Bridging the Gap Between Data and Insights<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Designing_an_Email_Management_Solution_Based_on_Open_Source_NLU\" title=\"Designing an Email Management Solution Based on Open Source NLU\">Designing an Email Management Solution Based on Open Source NLU<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#A_semantic_analysis-driven_customer_requirements_mining_method_%E2%80%A6_%E2%80%93_Naturecom\" title=\"A semantic analysis-driven customer requirements mining method &#8230; &#8211; Nature.com\">A semantic analysis-driven customer requirements mining method &#8230; &#8211; Nature.com<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Methods_and_algorithms\" title=\"Methods and algorithms\">Methods and algorithms<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#Training_the_word_embedding_model\" title=\"Training the word embedding model\">Training the word embedding model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/#What_is_semantic_and_pragmatic_analysis_in_NLP\" title=\"What is semantic and pragmatic analysis in NLP?\">What is semantic and pragmatic analysis in NLP?<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Results_and_analysis\"><\/span>Results and analysis<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>Sentiment analysis can also evaluate the effect of official crisis communication strategies. For this purpose, I often use a specific feature of the proprietary language called ANCESTOR. Sentiment analysis tools work by automatically detecting the tone, emotion, and turn of phrases and assigning them a positive, negative, or neutral label, so you know what types of phrases to use on your site. When a user types in the search \u201cwind draft\u201d, the whole point of the search is to find information about the current of air you can find flowing in narrow spaces. The challenge of the semantic analysis performed by the search engine will be to understand that the user is looking for a draft (the air current), all within a given radius.<\/p>\n<\/p>\n<div style='border: black dashed 1px;padding: 11px'>\n<h3><span class=\"ez-toc-section\" id=\"Text-based_automatic_personality_prediction_using_KGrAt-Net_a_%E2%80%A6_%E2%80%93_Naturecom\"><\/span>Text-based automatic personality prediction using KGrAt-Net: a &#8230; &#8211; Nature.com<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Text-based automatic personality prediction using KGrAt-Net: a &#8230;.<\/p>\n<p>Posted: Mon, 12 Dec 2022 08:00:00 GMT [<a href='https:\/\/news.google.com\/rss\/articles\/CBMiMmh0dHBzOi8vd3d3Lm5hdHVyZS5jb20vYXJ0aWNsZXMvczQxNTk4LTAyMi0yNTk1NS160gEA?oc=5' rel=\"nofollow\">source<\/a>]<\/p>\n<\/div>\n<p><p>However, these typical low-level features cannot effectively conserve rich visual information and fail to model the emotional content of images with various types. Recently, deep-learning methods like convolutional neural networks (CNNs) have been extensively applied to extract high-level features for visual sentiment analysis [8\u201310]. Nevertheless, because of the complexity and diversity of image emotion recognition, a large amount of labelled training data with small noise is required to achieve good performance. Besides, the CNNs are known as a black-box model in these works without elucidation.<\/p>\n<\/p>\n<p><h2><span class=\"ez-toc-section\" id=\"Sentiment_Analysis_vs_Semantic_Analysis_What_Creates_More_Value\"><\/span>Sentiment Analysis vs. Semantic Analysis: What Creates More Value?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>It involves using statistical and machine learning techniques to analyze and interpret large amounts of text data, such as social media posts, news articles, and customer reviews. Additionally, we also show the selected affective semantic concepts when the size of concept space is set to 300. Table 3 reports the top 30 concepts with the highest scores calculated by the proposed concept selection model. We divide them into object, scene and action semantics based on the image semantic hierarchy description.<\/p>\n<\/p>\n<div>\n<div>\n<h2><span class=\"ez-toc-section\" id=\"What_is_semantic_analysis_in_sentiment_analysis\"><\/span>What is semantic analysis in sentiment analysis?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<div>\n<div>\n<p>Semantic analysis is the study of the meaning of language, whereas sentiment analysis represents the emotional value.<\/p>\n<\/div><\/div>\n<\/div>\n<p><p>We will call these similarities negative semantic scores (NSS) and positive semantic scores (PSS), respectively. There are several ways to calculate the similarity between two collections of words. One of the most common approaches is to build the document vector by averaging over the document&#8217;s wordvectors. In that way, we will have a vector for every review and two vectors representing our positive and negative sets. The PSS and NSS can then be calculated by a simple cosine similarity between the review vector and the positive and negative vectors, respectively. Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer\u2019s inclination.<\/p>\n<\/p>\n<p><h2><span class=\"ez-toc-section\" id=\"Commercial_Products\"><\/span>Commercial Products:<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>Specifically for the task of irony detection, Wallace [23] presents both philosophical formalisms and machine learning approaches. The author argues that a model of the speaker is necessary to improve current machine learning methods and enable their application in a general problem, independently of domain. He discusses the gaps of current methods and proposes a pragmatic context model for <a href=\"https:\/\/metadialog.com\/\">metadialog.com<\/a> irony detection. Stavrianou et al. [15] also present the relation between ontologies and text mining. Ontologies can be used as background knowledge in a text mining process, and the text mining techniques can be used to generate and update ontologies. The advantage of a systematic literature review is that the protocol clearly specifies its bias, since the review process is well-defined.<\/p>\n<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='margin-left:auto;margin-right:auto' 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\" width=\"309px\" alt=\"semantics sentiment analysis\" \/><\/p>\n<p><p>Thesauruses, taxonomies, ontologies, and semantic networks are knowledge sources that are commonly used by the text mining community. Semantic networks is a network whose nodes are concepts that are linked by semantic relations. The most popular example is the WordNet [63], an electronic lexical database developed at the Princeton University. Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary [64]. Methods that deal with latent semantics are reviewed in the study of Daud et al. [16].<\/p>\n<\/p>\n<p><h2><span class=\"ez-toc-section\" id=\"Unveiling_the_Role_of_a_Full-Stack_Data_Scientist_Bridging_the_Gap_Between_Data_and_Insights\"><\/span>Unveiling the Role of a Full-Stack Data Scientist: Bridging the Gap Between Data and Insights<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>For the methods based on midlevel representation features, we compare with SentiBank [15] and the pretrained DeepSentibank [26]. SentiBank utilizes 1200-dimensional binary features detected by a concept detector library. While the DeepSentiBank is based on the CNN model to extract 2089-dimensional features and then perform image sentiment classification by a fully connected layer. For the methods based on high-level concepts, Ali et al. [29] proposed to use the pretrained AlexNet model on the ImageNet to extract 1000 dimensional objects features and on the Places dataset to obtain 365-dimensional scenes features. SnowNLP is a popular Python package in Chinese sentiment lexicon analysis in recent 2\u2009years (Huang et al., 2021).<\/p>\n<\/p>\n<ul>\n<li>Language detection\u00a0can detect the language\u00a0of written text and\u00a0report a single language code for\u00a0documents submitted\u00a0within a\u00a0wide range of languages, variants, dialects and some regional\/cultural languages.<\/li>\n<li>When features are single words, the text representation is called bag-of-words.<\/li>\n<li>Semantic analysis, a natural language processing method, entails examining the meaning of words and phrases to comprehend the intended purpose of a sentence or paragraph.<\/li>\n<li>As a result, this computation approach effectively eliminated the effects of common terms on keywords while also improving the association between keywords and articles (Che et al., 2021).<\/li>\n<li>Also, some of the technologies out there only make you think they understand the meaning of a text.<\/li>\n<li>\u201cCrisis communication on twitter during a global crisis of Volkswagen-The case of \u201cDieselgate\u201d.\u201d in Paper Presented at the Proceedings of the 51st Hawaii International Conference on System Sciences.<\/li>\n<\/ul>\n<p><p>Figure 4 displays the top five concepts with the highest scores of discriminativity excluding abstract nouns in eight emotion categories. It can be seen that these concepts almost conform to the important elements of human emotion perception. As shown in Figure 4, the score of the concept \u201croller coaster\u201d is 1.0 in the amusement category, which indicates it only appears in affective images with amusement emotion. Consequently, it coincides with the property of discriminativity defined in this paper. In the proposed (SALOM) model, the review is classified according to each exact product aspect and aspect synonym, hyponym, and hypernym.<\/p>\n<\/p>\n<p><h2><span class=\"ez-toc-section\" id=\"Designing_an_Email_Management_Solution_Based_on_Open_Source_NLU\"><\/span>Designing an Email Management Solution Based on Open Source NLU<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>The data used to support the findings of this study are available from the corresponding author upon request. Results of the A \u2212 ASyns \u2212 ARel aspect type for each lexicon are shown in bold. The idea of entity extraction is to&nbsp;identify named entities in text, such as names of people, companies, places, etc.<\/p>\n<\/p>\n<div style='border: black solid 1px;padding: 11px'>\n<h3><span class=\"ez-toc-section\" id=\"A_semantic_analysis-driven_customer_requirements_mining_method_%E2%80%A6_%E2%80%93_Naturecom\"><\/span>A semantic analysis-driven customer requirements mining method &#8230; &#8211; Nature.com<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A semantic analysis-driven customer requirements mining method &#8230;.<\/p>\n<p>Posted: Thu, 16 Jun 2022 07:00:00 GMT [<a href='https:\/\/news.google.com\/rss\/articles\/CBMiMmh0dHBzOi8vd3d3Lm5hdHVyZS5jb20vYXJ0aWNsZXMvczQxNTk4LTAyMi0xNDM5Ni0z0gEA?oc=5' rel=\"nofollow\">source<\/a>]<\/p>\n<\/div>\n<p><p>A \u201cstem\u201d is the part of a word that remains after the removal  of all affixes. For example, the stem for the word \u201ctouched\u201d is \u201ctouch.\u201d &#8220;Touch&#8221; is also the stem of \u201ctouching,\u201d and so on. Below is a parse tree for the sentence \u201cThe thief robbed the apartment.\u201d Included is a description of the three different information types  conveyed by the sentence. The second difficulty lies in the use of negative forms, usually recognizable by the joint use of the adverb &#8216;ne&#8217; and a particle &#8216;pas, none, plus&#8217;&#8230;<\/p>\n<\/p>\n<p><h2><span class=\"ez-toc-section\" id=\"Methods_and_algorithms\"><\/span>Methods and algorithms<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>Traditionally, text mining techniques are based on both a bag-of-words representation and application of data mining techniques. In order to get a more complete analysis of text collections and get better text mining results, several researchers directed their attention to text semantics. In Chinsha &amp; Joseph (2015), adjectives and verbs are used as sentiment words. More combinations are proposed as opinion phrases such as adverbs, adjective combinations, and adverb, verb combinations. Stanford dependency parser (Marneffe, MacCartney &amp; Manning, 2006) is used to extract the aspect related sentiment words from reviews.<\/p>\n<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='margin-left:auto;margin-right:auto' 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\" width=\"304px\" alt=\"semantics sentiment analysis\" \/><\/p>\n<p><p>The authors developed case studies demonstrating how text mining can be applied in social media intelligence. From our systematic mapping data, we found that Twitter is the most popular source of web texts and its posts are commonly used for sentiment analysis or event extraction. Overall, the integration of semantics and data science has the potential to revolutionize the way we analyze and interpret large datasets. By enabling computers to understand the meaning of words and phrases, semantic analysis can help us extract valuable insights from unstructured data sources such as social media posts, news articles, and customer reviews.<\/p>\n<\/p>\n<p><h2><span class=\"ez-toc-section\" id=\"Training_the_word_embedding_model\"><\/span>Training the word embedding model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/p>\n<p><p>It gives computers and systems the ability to understand, interpret, and derive meanings from sentences, paragraphs, reports, registers, files, or any document of a similar kind. Semantic analysis is defined as a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. This article explains the fundamentals of semantic analysis, how it works, examples, and the top five semantic analysis applications in 2022. Finally, sentiment analysis was used to judge the positivity and negativity of these texts. Since NetEase officially issued three statements, the changes of text content and emotional polarity in the comments are the keys to evaluating whether crisis communication strategies impact users\u2019 attitudes.<\/p>\n<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='margin-left:auto;margin-right:auto' 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\" width=\"301px\" alt=\"semantics sentiment analysis\" \/><\/p>\n<p><p>As shown in Figure 2, we were only interested in the content of the text (in the blue box), and the rest of the data were deleted, such as posts ID, time of release, etc. Then, using MS Excel, non-essential columns were removed, including timestamps and user nicknames. For instance, while working with Studio, I discovered that this knowledge graph contains a huge collection of verbs, adjectives and noun chains with dozens of commonly used words that reflect a negative opinion.<\/p>\n<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='margin-left:auto;margin-right:auto' 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[15] proposed to utilize Adjectives Noun Pairs (ANPs), which were explored based on strong co-occurrence relationships with emotion tags of web images&#8230;.<\/p>\n","protected":false},"author":26,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-3626","post","type-post","status-publish","format-standard","hentry","category-digital-marketing"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AIS Electronic Library AISeL ICIS 2015 Proceedings: Sentiment Analysis Meets Semantic Analysis: Constructing Insight Knowledge Bases - CEOweb Ltd. Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AIS Electronic Library AISeL ICIS 2015 Proceedings: Sentiment Analysis Meets Semantic Analysis: Constructing Insight Knowledge Bases - CEOweb Ltd. Blog\" \/>\n<meta property=\"og:description\" content=\"Borth et al. [15] proposed to utilize Adjectives Noun Pairs (ANPs), which were explored based on strong co-occurrence relationships with emotion tags of web images....\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ceowebltd.com\/blog\/ais-electronic-library-aisel-icis-2015-proceedings\/\" \/>\n<meta property=\"og:site_name\" content=\"CEOweb Ltd. 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