The operation of SOM is based on competition and neighborhood. This network consists of a set of output neurons (points) located in two or three dimensional space. Each point in the map is connected to a weight vector. During training, the input vector is presented to the network and each neuron determines the winner of the competition by calculating the distance between the input vector and its weight vector. Then, the winning neuron and its neighboring neurons continuously update the weights to properly represent the input vector.

The main application of SOM neural network is in pattern analysis, data dimensionality reduction and clustering analysis. By using SOM, it is possible to identify hidden patterns in the data and visualize the data in a low-dimensional (usually two-dimensional) space. This network is usually used in the field of image analysis, natural language processing, text data analysis and molecular data. In summary, SOM neural network is an efficient method for data analysis and dimensionality reduction in order to identify hidden patterns and represent data in low-dimensional space.

How can I use SOM neural network to analyze text data?

SOM neural network is a powerful method for text data analysis. To use SOM neural network in analyzing text data, you should follow the steps below.

 Data preprocessing: In this step, provide your textual data in a suitable form as input to the SOM neural network. This process includes various steps such as cleaning (removing punctuation marks, symbols, stop words, etc.), tokenization (dividing text into tokens or smaller units such as words or n-grams) and feature extraction (such as feature vectors such as TF-IDF). will be

 Structuring the network: In this step, you need to define the structure of the SOM neural network. The number and size of neurons in the map, the number of different dimensions (usually 2 or 3 dimensions) and other parameters such as the learning rate and the number of training cycles are specified.

 Train the network: In this step, you feed the pre-processed data to the SOM neural network and train the network. In each training session, you provide the input vector to the neurons and the neuron determines the winner of the competition, and then the weights of the winning neurons and the neighbors are continuously updated so that the data is well represented in the map.

 Analyze the results: After training the network, you can use the SOM neural map to analyze textual data. Based on the location of the data in the map, you can identify similar text patterns and perform clustering. Also, by displaying the data in low-dimensional space, you can visualize the results.

Using the SOM neural network in textual data analysis can help you identify and analyze hidden patterns and relationships in the data. You can use it for text clustering, text categorization, topic analysis, sentiment classification, and other tasks related to natural language processing.

To implement a SOM neural network, you can use libraries and tools available in different programming languages ​​such as Python, R or MATLAB. Some popular Python libraries for SOM neural network are MiniSom, SOMPY and Kohonen. These libraries provide tools that help you build and train a SOM neural network.

By using SOM neural network, you can represent textual feature vectors in low-dimensional space and identify hidden patterns. This approach allows you to display textual data graphically and understandably and perform analysis based on them. Finally, to effectively use the SOM neural network in textual data analysis, it is important to carefully determine the data preprocessing steps and network settings, and carefully evaluate the results.

Some practical examples of SOM neural network in textual data analysis

SOM neural network is used in analyzing textual data in many fields. The first is text clustering. Using the SOM neural network, you can group text data into similar clusters. By applying SOM on the text feature vectors, the data is mapped and clusters of text data with similar features are located nearby. The above technique allows you to identify similar patterns and similar topics in textual data. For example, you can cluster news into different categories based on their topics, such as sports, politics, technology, etc.

SOM neural network can be useful in topic analysis in textual data. Using SOM, you can identify different themes in textual datasets. By displaying textual data in a SOM map, you can view an infinite number of different topics and identify topics that are similarly mapped as a common topic.

Also, using the SOM neural network, you can categorize text data based on sentiment (positive, negative, neutral). By training the SOM with sentiment labels, you can give the network new textual data as input and use the network to recognize their sentiment. This technique allows you to get an overview of users' opinions and feelings about products, services, etc. It is not bad to know that the SOM neural network