Natural Language Processing (NLP) is a subfield of computer science that encompasses the interaction between computers and human language, and is one of the important areas of artificial intelligence. With the advancement of deep learning technologies, NLP is greatly helping to address various problems. In particular, Doc2Vec is one of the effective methodologies for calculating the similarity between documents by mapping the meaning of documents into vector space, and it is utilized in many studies. This article will discuss how to calculate the similarity of public business reports using Doc2Vec.<\/p>\n
The advancement of natural language processing is becoming increasingly important in various fields such as business, healthcare, and finance. Especially in processing large amounts of unstructured data like public business reports, NLP technology is essential. By evaluating the similarity between documents, companies can analyze their competitiveness and support decision-making.<\/p>\n
Unstructured data refers to data that does not have a standardized format. Unstructured data, which exists in various forms such as public business reports, news articles, and social media posts, is very important for evaluating and analyzing company value. Analyzing this unstructured data requires advanced NLP technology.<\/p>\n
Traditional NLP methods primarily used statistical techniques and rule-based approaches, but in recent years, deep learning-based models have gained a lot of attention. In particular, embedding techniques such as Word2Vec and GloVe capture meaning by mapping words into high-dimensional vector spaces, and Doc2Vec extends this technology to the document level.<\/p>\n
Doc2Vec is a model developed by researchers at Google that maps documents into high-dimensional vector spaces. This model is based on two main ideas: (1) each word has a unique vector, and (2) documents also have unique vectors. This allows for the calculation of similarity between documents.<\/p>\n
The Doc2Vec model uses two variants: Distributed Bag of Words (DBOW) and Distributed Memory (DM) methods. The DBOW method predicts words based only on the document vector, while the DM method uses both word and document vectors to predict the next word. By combining these two methods, richer document representations can be obtained.<\/p>\n
The learning process of Doc2Vec proceeds through a large corpus of text data. Documents and words are provided together, and the model learns a unique vector for each document. Once trained, this vector can be used to compare the similarity between documents.<\/p>\n
Public business reports are important documents that communicate a company’s financial status and management performance to shareholders. These documents exist in large quantities and are essential materials for long-term company analysis. However, these documents are composed of unstructured data, which has limitations under simple text analysis.<\/p>\n
Public business reports typically include the following components:<\/p>\n
By analyzing this information using natural language processing techniques, the similarity between documents can be evaluated.<\/p>\n
The process of calculating the similarity of public business reports involves several steps. This procedure includes data collection, preprocessing, training the Doc2Vec model, and similarity calculation.<\/p>\n
Public business reports must be collected from various modern information sources. Mechanical collection methods include web scraping and using APIs, which can secure data in various formats.<\/p>\n
The collected data must be organized into document form through preprocessing. Typical preprocessing steps include:<\/p>\n
Through these processes, the meanings of the words can be clarified, enhancing the training efficiency of the Doc2Vec model.<\/p>\n
After preprocessing, the Doc2Vec model is trained. Using the gensim library in Python, the Doc2Vec model can be efficiently created. Here is a sample code:<\/p>\n
import gensim\n\nfrom gensim.models import Doc2Vec\n\nfrom nltk.tokenize import word_tokenize\n\n\n\n# Load data\n\ndocuments = [...] # Preprocessed business report data list\n\ntagged_data = [gensim.models.doc2vec.TaggedDocument(words=word_tokenize(doc), tags=[str(i)]) for i, doc in enumerate(documents)]\n\n\n\n# Initialize and train the Doc2Vec model\n\nmodel = Doc2Vec(vector_size=20, min_count=1, epochs=100)\n\nmodel.build_vocab(tagged_data)\n\nmodel.train(tagged_data, total_examples=model.corpus_count, epochs=model.epochs)<\/code><\/pre>\n4.4 Similarity Calculation<\/h3>\n
After the model training is complete, the vectors for each business report document are extracted, and the similarity between the documents is calculated. The gensim library can be used to easily analyze similarity:<\/p>\n
# Similarity calculation\n\nsimilarity = model.wv.n_similarity([\"Content of Business Report 1\"], [\"Content of Business Report 2\"])<\/code><\/pre>\nUsing the code above, the similarity between the two documents can be obtained as a value between 0 and 1. A value closer to 1 indicates a higher similarity between the two documents.<\/p>\n
5. Results and Analysis<\/h2>\n
The analysis results of the model numerically indicate the similarity between public business reports, which can be used in business and financial analysis. For example, two documents showing high similarity may belong to similar industries or reflect similar decisions.<\/p>\n
5.1 Visualization of Results<\/h3>\n
It is also important to visualize the calculated similarity results for analysis. Libraries like matplotlib and seaborn can be used to carry out data visualization:<\/p>\n
import matplotlib.pyplot as plt\n\nimport seaborn as sns\n\n\n\n# Create data frame\n\nimport pandas as pd\n\n\n\nsimilarity_data = pd.DataFrame(similarity_list, columns=['Document1', 'Document2', 'Similarity'])\n\nsns.heatmap(similarity_data.pivot(\"Document1\", \"Document2\", \"Similarity\"), annot=True)<\/code><\/pre>\n6. Conclusion<\/h2>\n
Calculating similarity using Doc2Vec has become a very useful tool in analyzing unstructured data such as public business reports. With deep learning-based natural language processing technologies, the quality of company analysis can be improved, supporting more effective decision-making. In the future, more sophisticated models may contribute to in-depth analysis and predictive modeling of public business reports.<\/p>\n
7. References<\/h2>\n