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5 minutes de lecture
Language use or discourse are often tricky processes to decipher. Saying even a simple expression in a different tone or in a different place and time changes the understanding of this expression. Some logical processes, such as context and coherence, can help make sense of this situation, but how do you do this in a long, voluminous text? This is where text analysis comes into play.
Let’s start with the definition
Text analysis (content or textual analysis) is a research method usually supported by artificial intelligence software that finds meanings, patterns, and relationships in any text.
It enables the smooth and systematic classification of incredibly dense and unstructured text data into well-structured ones. It can examine historical or real-time data with qualitative and quantitative techniques and provide definitive evidence for the purpose of the research.
Techniques of the text analysis
In order to carry out your research for more accurate purposes, you may need to choose the appropriate text analysis models and techniques. For example, examining survey answers and examining the transcript of a conversation will not serve the same purposes.
Text analysis techniques
Likewise, an analysis made by businesses to understand their customers is not the same as a linguistic description made for a thesis. You can also use similar analysis methods like content analysis and thematic analysis to identify text data. Here are text analysis examples for each type listed below, from which you can choose the one that suits you:
- Named entity recognition(NER)
This technique is used to analyze a text according to pre-defined categories. For example, you create categories such as proper names, time, and place, and the program you use classifies the expressions that fit these patterns for you.
Example: “It has been 1 year, but this application still does not work in cities other than London.”
Analysis: 1 year(time), cities other than London(location)
This technique is used to identify and extract the emotions of people from any qualitative data. It is generally utilized by businesses to enhance customer satisfaction. There are also ways to do this, such as using a mood scale(1 to 10) or identifying only the dominant emotion.
Example: “I have seen many different TV brands, but this one really made me satisfied while watching it.”
Analysis: Highly positive, satisfaction feeling
- Linguistic analysis
It is a technique that tries to reveal how natural language is used in a text, whether there are grammatical errors, and which functions of the language are used. It is a good method to better understand the relationships and patterns between sentences and to find contextual foundations.
Example: “When you look at the latest movies, they are all good movies.”
Syntactic analysis: The subject is “they,” the verb is “are,” the object is “all good movies,” and the subordinate clause is “when you look at the latest movies.”
Semantic analysis: The sentence expresses a general observation or assertion about the quality of "the latest movies."
- Quantitative analysis
Quantitative analysis is a technique used to examine the patterns of phrases and expressions in a text according to their frequency or to examine numerical data. For this purpose, it is especially appropriate to examine objective data.
Example: You conducted a survey of user comments about a newly released product.
Analysis: When you listed the words that appeared most frequently in the survey responses, you discovered that the words "useful," "affordable," and "good" were the most frequently mentioned.
Text analysis vs. Text mining
First of all, text analysis is a research method, but text mining is a data extraction process. Therefore, it is useful to know that they do not mean the same thing. Text analysis is generally used to find patterns and valuable insights from any text.
This can be used to prove a theory or establish a theory, and the course of the analysis can be shaped accordingly. But text mining is a data mining process. The purpose is only to reveal the data that is not visible, cluster it, classify it, and perform similar operations.
Text analysis as a method for enterprises
In the business world, where any kind of data analysis is worth its weight, it is of great importance to understand what people think and express. You can ensure with a proper analysis that both employees, customers, and the business in general are in a better position. You can use text analysis for this, and why you should use it is listed below:
- It improves customer experience. Businesses can take advantage of customer reviews and personalize their overall experience.
- It is a must for quality product development. By better understanding your customers' preferences, you can develop products and services that meet their needs.
- You can strengthen your marketing and get ahead of your competitors by using business intelligence tools and conducting text analysis.
- Text analysis helps you in risk management. It provides you with data that will enable you to make appropriate decisions for your business by understanding what current and future problems are.
Benefits of the text analysis
The benefits textual data has for businesses are mentioned before. However, it is important to remember that it can be used in all kinds of research activities, not just for businesses. Because text analysis is a guide that will help you when you are lost, it clarifies many questions thanks to the data it reveals. To broaden the benefits examples:
Reasons to use the text analysis
- Understanding social media: There is no limit to the texts that text analysis can examine. Many different texts, from blog posts to news articles, user comments, and social media conversations, serve as data sources for research. Since social media is one of the most powerful tools for communicating with people, you should analyze many different text types as often as possible.
- Providing actionable insights: Like many types of analysis, text analysis provides you with solid data that will enable you to take the necessary steps toward your goal.
- Solving problems and secrets: Text analysis is a method that reveals hidden meanings and patterns in any content. For this reason, machine learning models and AI text analysis will be more useful because they produce more successful and error-free results than text analysis conducted by humans.
Text analysis tools
There are many different text analysis software and tools that researchers can use depending on their needs and competence. Here just the popular ones are listed below:
Tools for text analysis
- Natural language toolkit(NLTK): NLTK is a Python library that you can use in text analysis. It makes your analysis easier with the many different tools it provides.
- Google Cloud Platform: Thanks to Google’s NLP tool, you can easily perform many text analysis techniques, from sentiment analysis to linguistic analysis.
- MonkeyLearn: MonkeyLearn is a valuable tool that allows you to perform analyses without writing code, thanks to its user-friendly interface and the models and integrations it provides.
- SpaCy: This is another Python library you can use in text analysis. It is especially preferred over the others due to its ease of use and performance.
Frequently asked questions about the text analysis
In this FAQ section, you can easily access the parts about text analysis that you and other readers are curious about.
Par exemple, l'analyse de texte est un outil essentiel pour la publicité. L'analyse des commentaires sur les médias sociaux, de l'historique des recherches et des textes similaires basés sur les cookies dans tout environnement numérique est d'une grande importance pour la poursuite des activités publicitaires.
Pour savoir comment rédiger une analyse de texte, il faut d'abord comprendre les principaux éléments du texte de recherche, tels que les thèmes et les modèles. Cela est possible grâce à une analyse correcte et à l'évaluation d'un chercheur qualifié.
Ensuite, les opinions et les évaluations liées au sujet sont présentées à l'aide de preuves et d'exemples tirés du texte. Enfin, un avis consultatif est présenté en énonçant l'objectif principal et le résumé de l'analyse.
L'analyse des types de textes permet de déterminer à quel genre un texte appartient et quels sont ses schémas linguistiques. Cela permet aux chercheurs de comprendre les structures avec lesquelles les différents types de textes émergent et comment ils sont utilisés dans la communication.
Oui, l'analyse de texte est une méthode de recherche qui permet d'analyser tout langage écrit ou oral transcrit afin d'obtenir des informations utiles. Cependant, il n'y a pas qu'une seule façon de procéder et elle fait appel à de nombreux autres types de recherche.
L'analyse de texte est une méthode de recherche qui permet d'utiliser non seulement des données qualitatives, mais aussi des données quantitatives. En fonction de l'objectif des questions de recherche, l'accent peut être mis sur les données qualitatives ou quantitatives, ou il est possible d'utiliser les deux consécutivement.
En linguistique, l'analyse de texte est utilisée pour comprendre la fonction d'une langue, d'un discours et d'une communication. Le texte est examiné dans différentes dimensions selon les sous-branches de la linguistique. Alors que les caractéristiques des sons sont importantes dans l'analyse phonologique des textes, les règles de construction des mots sont étudiées dans l'analyse morphologique.
En outre, l'analyse syntaxique est effectuée dans la dimension de la phrase, l'analyse sémantique dans la dimension du sens, l'analyse pragmatique dans l'utilisation de la langue et l'analyse du discours dans la dimension du discours à grande échelle.
L'analyse de texte et l'analyse de sentiments sont toutes deux utilisées dans le traitement du langage naturel (NLP). L'analyse des sentiments est une sous-catégorie de l'analyse de texte. Elle est particulièrement utilisée pour évaluer et noter l'état émotionnel dominant dans un texte.
Elle est généralement utilisée par les entreprises et les organisations pour mieux comprendre les sentiments et les pensées des clients et du public. Cependant, l'analyse de texte est généralement utilisée pour révéler des concepts, des expressions et des structures dans un texte, et son objectif est plus large que l'examen des émotions.
L'analyse de texte et l'analyse de document sont deux méthodes différentes qui sont liées l'une à l'autre mais qui ont des objectifs différents. La principale différence entre l'analyse de documents et l'analyse de textes est que la première est utilisée pour examiner la structure de textes à grande échelle, tandis que la seconde est utilisée pour examiner un texte individuel.
La taille de ce texte individuel peut également être très importante, mais l'analyse de texte tente de comprendre l'essence des modèles et des expressions de ce texte. D'autre part, l'analyse de documents est utilisée pour classer et résumer le document en examinant structurellement tous les éléments à l'intérieur et à l'extérieur du texte.
Final words
In conclusion, the purpose of this article is to introduce you to text analysis and make you a competent individual in this type of analysis. Therefore, first of all, the introduction was made with the definition of text analysis. Then, the techniques you could use were explained. Since it is often confused with text mining, its differences were mentioned.
The benefits for businesses and anyone wanting to do general research are listed. Finally, the article ended by mentioning the software tools with which you can analyze text. After that, all you have to do is start creating a custom text analysis.
Atakan is a content writer at forms.app. He likes to research various fields like history, sociology, and psychology. He knows English and Korean. His expertise lies in data analysis, data types, and methods.
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