Text Me Up

Text HOME to 741741 from anywhere in the United States, anytime. Crisis Text Line is here for any crisis. A live, trained Crisis Counselor receives the text and responds, all from our secure online platform. The volunteer Crisis Counselor will help you move from a hot moment to a cool moment.

The TEXT function lets you change the way a number appears by applying formatting to it with format codes. It's useful in situations where you want to display numbers in a more readable format, or you want to combine numbers with text or symbols.

Text Me Up

Download File 🔥 https://urllie.com/2y2FKX 🔥

Excel is trained to look for numbers being entered in cells, not numbers that look like text, like part numbers or SKU's. To retain leading zeros, format the input range as Text before you paste or enter values. Select the column, or range where you'll be putting the values, then use CTRL+1 to bring up the Format > Cells dialog and on the Number tab select Text. Now Excel will keep your leading 0's.

Most notably: clip-path, clip-rule, color, color-interpolation, color-rendering, cursor, display, dominant-baseline, fill, fill-opacity, fill-rule, filter, mask, opacity, pointer-events, shape-rendering, stroke, stroke-dasharray, stroke-dashoffset, stroke-linecap, stroke-linejoin, stroke-miterlimit, stroke-opacity, stroke-width, text-anchor, transform, vector-effect, visibility

Important: High contrast is very important for people with vision problems. So, always ensure that the contrast between the text color and the background color (or background image) is good!

Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources."[1] Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process.[2] Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via the application of natural language processing (NLP), different types of algorithms and analytical methods. An important phase of this process is the interpretation of the gathered information.

A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. The document is the basic element when starting with text mining. Here, we define a document as a unit of textual data, which normally exists in many types of collections.[3]

Text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.[4] The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining"[5] in 2004 to describe "text analytics".[6] The latter term is now used more frequently in business settings while "text mining" is used in some of the earliest application areas, dating to the 1980s,[7] notably life-sciences research and government intelligence.

Text mining technology is now broadly applied to a wide variety of government, research, and business needs. All these groups may use text mining for records management and searching documents relevant to their daily activities. Legal professionals may use text mining for e-discovery, for example. Governments and military groups use text mining for national security and intelligence purposes. Scientific researchers incorporate text mining approaches into efforts to organize large sets of text data (i.e., addressing the problem of unstructured data), to determine ideas communicated through text (e.g., sentiment analysis in social media[14][15][16]) and to support scientific discovery in fields such as the life sciences and bioinformatics. In business, applications are used to support competitive intelligence and automated ad placement, among numerous other activities.

Many text mining software packages are marketed for security applications, especially monitoring and analysis of online plain text sources such as Internet news, blogs, etc. for national security purposes.[17] It is also involved in the study of text encryption/decryption.

A range of text mining applications in the biomedical literature has been described,[19] including computational approaches to assist with studies in protein docking,[20] protein interactions,[21][22] and protein-disease associations.[23] In addition, with large patient textual datasets in the clinical field, datasets of demographic information in population studies and adverse event reports, text mining can facilitate clinical studies and precision medicine. Text mining algorithms can facilitate the stratification and indexing of specific clinical events in large patient textual datasets of symptoms, side effects, and comorbidities from electronic health records, event reports, and reports from specific diagnostic tests.[24] One online text mining application in the biomedical literature is PubGene, a publicly accessible search engine that combines biomedical text mining with network visualization.[25][26] GoPubMed is a knowledge-based search engine for biomedical texts. Text mining techniques also enable us to extract unknown knowledge from unstructured documents in the clinical domain[27]

Text mining methods and software is also being researched and developed by major firms, including IBM and Microsoft, to further automate the mining and analysis processes, and by different firms working in the area of search and indexing in general as a way to improve their results. Within the public sector, much effort has been concentrated on creating software for tracking and monitoring terrorist activities.[28] For study purposes, Weka software is one of the most popular options in the scientific world, acting as an excellent entry point for beginners. For Python programmers, there is an excellent toolkit called NLTK for more general purposes. For more advanced programmers, there's also the Gensim library, which focuses on word embedding-based text representations.

The issue of text mining is of importance to publishers who hold large databases of information needing indexing for retrieval. This is especially true in scientific disciplines, in which highly specific information is often contained within the written text. Therefore, initiatives have been taken such as Nature's proposal for an Open Text Mining Interface (OTMI) and the National Institutes of Health's common Journal Publishing Document Type Definition (DTD) that would provide semantic cues to machines to answer specific queries contained within the text without removing publisher barriers to public access.

The automatic analysis of vast textual corpora has created the possibility for scholars to analyzemillions of documents in multiple languages with very limited manual intervention. Key enabling technologies have been parsing, machine translation, topic categorization, and machine learning.

The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analyzed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.[44] This automates the approach introduced by quantitative narrative analysis,[45] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.[43]

Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents.[46][47][48][49][50] The analysis of readability, gender bias and topic bias was demonstrated in Flaounas et al.[51] showing how different topics have different gender biases and levels of readability; the possibility to detect mood patterns in a vast population by analyzing Twitter content was demonstrated as well.[52][53] ff782bc1db