Classification of Texts by Artificial Intelligence

The classification of texts through Natural Language Processing (NLP) in artificial intelligence can be a valuable tool in sustainability projects. Here are some ways this technology can be applied:

1. Sentiment analysis: This allows you to identify people's opinions regarding environmental issues, conservation, renewable energies, among others. This analysis can be used to measure the effectiveness of awareness campaigns and understand people's attitudes and concerns about sustainability.
2. Classification of documents: With NLP, it is possible to classify documents related to sustainability in specific categories, such as “renewable energy”, “recycling”, “water conservation”, “environmental education”, among others. This facilitates the organization and analysis of large volumes of information, allowing you to identify areas of focus, trends and knowledge gaps.
3. Social media monitoring: Through the analysis of texts on social media platforms, NLP can identify trends and patterns of behavior related to sustainability. This information can be used to measure awareness, engagement and public opinion on environmental issues, as well as to identify opportunities for intervention and dialogue.
4. Environmental impact analysis: NLP can help analyze reports, studies and technical documents related to sustainability projects. This can include identifying environmental impacts, assessing the effectiveness of mitigation measures, comparing alternatives and identifying best practices. Automated analysis of large volumes of information enables faster and more efficient processing, facilitating informed decision-making.

Let's see what are the stages of this type of processing modality in artificial intelligence.

the acquisition of information

An essential step that will be decisive for the quality of the artificial intelligence algorithm is the way in which we collect information and organize it. In this model, we need to have a background information base with an indicated classification. We can, for example, use a database of reports about a certain region to structure an algorithm to understand and predict the classifications of new entries. In the case of sentiment analysis, understand, for example, whether a manifestation on a social network is positive. In document classification, we will be able to understand which category new entries fit into. And so on.

The vectorization of the text

To allow text processing we need to transform it into a model understandable by the computer. And this is where an algorithm like TfIdf Vectorizer comes in.

Tfidf” is an abbreviation for “Term Frequency-Inverse Document Frequency”. It is a method that converts a set of text documents into a numeric representation, making them suitable for machine analysis. . It calculates the relative importance of each word in a document, based on its frequency in the specific document and overall frequency across the entire corpus (set of documents). The goal is to capture the importance of a specific term for a specific document, balancing that with its general importance across the entire document set.

The TfidfVectorizer process involves two main steps:

1. Term Frequency (TF): Calculates the frequency of each term (word) in a specific document. Generally, a simple counting scheme is used, where the value represents the number of times a term occurs in the document.
2. Inverse Document Frequency (IDF): Calculates the overall importance of a term across the entire document set. It is a logarithmic measure of the inverse proportion of documents that contain the term. Terms that appear in many documents have a lower IDF, while those that appear in fewer documents have a higher IDF.

By multiplying the term frequency (TF) by the inverse of the document frequency (IDF), we get the TF-IDF value for each term in a document. The higher the TF-IDF value, the more relevant the term is for that specific document.

TfidfVectorizer performs these calculations automatically for each term in an artificial intelligence text. It converts documents into numeric arrays, where each array element represents a specific term and its corresponding TF-IDF value. This allows machine learning techniques to be applied to these vectors for tasks such as text classification, clustering, information extraction, and more.

Binary classification of texts with artificial intelligence

As our objective in this text processing in artificial intelligence is to classify it from predefined categories, we use a binary classification model – that is, we will indicate, for each category, if the text belongs to it or not.

For this we will use One Vs Rest. The goal of the OvR is to train a separate classifier for each class in a multiclass classification problem. Each classifier is trained to distinguish a specific class from all other classes. During the training phase, a classifier is trained with positive examples from the target class and negative examples from all other classes. This process is repeated for each class in the dataset.

During the testing phase, when a new sample needs to be classified, each classifier is applied to the sample and makes a binary prediction. The class assigned by the classifier that obtained the most confident result is selected as the final sample class.

How to evaluate the artificial intelligence algorithm

Hamming Loss and Accuracy are two metrics frequently used in the evaluation of Natural Language Processing (NLP) algorithms that deal with multi-label classification tasks. Let's compare these two methods:

1. Hamming Loss:

• Hamming Loss measures the average fraction of mislabels for each test sample.
• It is a suitable metric to assess the overall accuracy of the model in the multi-label classification task.
• Hamming Loss considers each label separately, ignoring interactions between labels.
• The Hamming Loss ranges from 0 to 1, with 0 indicating perfect performance (no mislabels) and 1 indicating poor performance (all mislabels).

1. Accuracy:

• Accuracy is a common and widely used measure to assess the accuracy of a model in classification problems.
• Accuracy measures the fraction of correctly classified test samples relative to the total samples.
• Accuracy is an appropriate metric for multiclass classification tasks, where only one label is assigned to each sample.
• Accuracy does not take into account individual mislabels in multi-label classification problems.

The Hamming Loss is a specific metric for multi-label classification problems, considering the average fraction of incorrect labels per sample. It evaluates the overall accuracy of the model, but does not take interactions between labels into account. Accuracy is a widely used metric in classification problems, suitable for multiclass classification tasks, but it does not consider individual mislabels in multilabel classification problems. Both metrics have their applications and limitations, and the choice between them depends on the nature of the NLP task and the specific evaluation objectives.