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SENTIMENT | Analysis of Meaning and Emotion in Texts

Sentiment is a prominent solution among text analysis systems. By offering high success rates in both Turkish and English languages, it makes text analysis processes faster and easier. Sentiment conducts open-ended analyses using various methods and helps you gain insights into people’s attitudes, behaviors, concerns, motivations, and cultures.

One of the greatest benefits of Sentiment is reducing data analysis processes dependent on human resources. This results in obtaining healthier and more reliable outcomes. Sentiment is a product that can be used in both public institutions and the private sector. It is also utilized in classifying speech outputs obtained from voice data based on content, topic, and emotion.

Sentiment software performs data analysis using text mining methods. During the analysis process, a training dataset is created using textual data with previously known sentiment (positive/negative/neutral). The provided data undergo preprocessing steps such as removing symbols and punctuation marks from the text, tokenization to separate text into words and finding the roots of each word to create term lists, removing stop words consisting of prepositions, conjunctions, and pronouns, and creating a vector space model using term frequencies and inverse document frequencies.

Sentiment software is highly significant today. With the widespread use of the internet and the popularity of social media platforms, people communicate through texts more frequently. Therefore, many institutions and organizations need to conduct text analysis to understand the emotions and thoughts of their customers or target audience.

For example, a company can gather feedback on their products or services by analyzing customer comments on social media platforms. Similarly, a political party can analyze social media and survey data to understand voters’ attitudes.

Sentiment software is crucial for such analyses. Especially, sentiment software enables the analysis of billions of lines of data without relying on human resources, leading to accurate results. Additionally, sentiment software helps make sense of data, allowing organizations to better understand customer behaviors and develop more effective marketing strategies.

Moreover, the ability of sentiment software to quickly adapt to new languages is a significant advantage for international businesses. This enables sentiment software to analyze texts in different languages worldwide, providing a global perspective.

In conclusion, sentiment software is widely used today in various fields such as social media, news websites, blogs, surveys, and emails. Through sentiment software, organizations can understand the emotions and thoughts of their customers or target audiences, enabling them to develop more effective strategies.

Sentiment can assist organizations in tracking opinions and satisfaction levels of their customers by analyzing texts from social media sites, news websites, blogs, forums, and other open sources. For instance, an organization can measure their customers’ sentiments and evaluate feedback by monitoring posts related to a product or service on social media platforms.

Social media analysis can also be utilized to gauge sensitivity on societal issues such as elections or other significant events. This analysis is particularly crucial for political campaigns. Political candidates can learn about voters’ thoughts, feelings, and important issues by conducting social media analysis.

Moreover, social media analysis can be used to develop marketing strategies by identifying trends and habits of customers or target audiences. Companies can increase customer loyalty and boost sales by measuring how their products or services are perceived.

Sentiment can also help organizations track news about their products or services by analyzing texts from news websites, forums, and other open sources. This enables companies to better plan crisis management by anticipating negative news in advance.

In conclusion, Sentiment’s ability to analyze texts from social media and other open sources can help organizations gain important insights about their customers and target audiences, develop crisis management plans, and formulate marketing strategies. Therefore, this analytical tool is a valuable asset for organizations in analyzing texts from social media and other open sources.


Sentiment helps corporate companies understand customer feedback, complaints, and satisfaction by analyzing data from all open sources. Within this scope, customer reviews, complaints and suggestions, social media posts, customer surveys, forum and blog comments, news articles, emails, and many other sources are analyzed by Sentiment to provide insights for companies to improve their services and products and enhance customer satisfaction.

Especially, reviews, product evaluations, customer ratings, and feedback on shopping websites are analyzed by Sentiment to identify actions that companies can take to increase customer satisfaction and loyalty. Additionally, Sentiment can be used for assessing the suitability of company products for the target audience, marketing strategies, and competitor analysis.

Corporate companies may also be interested in obtaining information about employee opinions and feedback. In this regard, Sentiment can provide insights into employee satisfaction levels, opinions, and concerns by analyzing sources such as internal surveys, employee emails, and social media posts.


In today’s world, messages and posts on open sources are of vital importance for national security. Various malicious actors such as terrorist organizations, extremist groups, and cybercriminals utilize communication channels on social media and other open sources to conduct threats and propaganda activities. Therefore, security agencies need to analyze data from these sources and detect potential threats in advance.

Sentiment provides a solution in this field by analyzing messages and posts on open sources to detect potential threats and assist in taking preventive measures. The software helps security agencies operate more effectively by monitoring communication channels of terrorist organizations, identifying propaganda of extremist groups, and identifying threats related to cyber attacks.

As a result, security agencies, which play an important role in national security, can detect potential threats in advance and take rapid and effective measures with the help of Sentiment. Additionally, the use of Sentiment reduces the workload on human resources, enabling data to be analyzed faster and more reliably. This in turn allows security agencies to operate more efficiently and effectively.

In conclusion, Sentiment provides significant support to security agencies in the analysis of messages and posts on open sources, playing a vital role in national security.


Natural language processing applications require a large amount of language data. Data sets are the most important factor influencing the success of these applications. This is because the diversity and quality of language data directly impact the performance of natural language processing applications.

Sentiment is not limited to narrow data scope such as social media messages alone. It can also analyze a wide range of data including news websites, blogs, surveys, emails, and more.

Sentiment can analyze both spoken and written data with high accuracy rates, thereby preventing misinterpretations.

Sentiment fully automates the text analysis process, eliminating the human factor. This ensures analyses are conducted faster and more reliably.

Sentiment can create customized analysis models tailored to different sectors thanks to its end-to-end customization feature. This enables tailored analyses to meet specific needs in different sectors.

Sentiment has the ability to quickly analyze large datasets. This allows it to access and analyze billions of rows of data rapidly.

Sentiment provides a flexible Application Programming Interface (API), allowing easy integration with various applications and systems.

Natural language processing applications analyze data and produce results using machine learning algorithms. These algorithms are a subset of artificial intelligence technologies and are trained to produce accurate results. Therefore, the success of natural language processing applications depends on the quality of the machine learning algorithms used.

Natural language processing applications make use of language models to understand texts. Language models form the foundation of natural language processing applications and must be trained accurately to obtain correct results.

Natural language processing applications require preprocessing steps such as cleaning, organizing, and summarizing data. These steps ensure that the data is properly analyzed and enhance the success of natural language processing applications.