Gemshim (what company is gem in the United States)

Complete Game Information：

• 1、How to incrementally train TF-IDF and LDA models in Gensim?
• 2 、Please give me a transliteration of the song An Qixuan (Mask). Thank you for your help.
• 3
  、Natural Language Processing-Using Word Vector (Tencent Word Vector)
• 4、Big data analysis sets sail
• 5 、The number of LDA topics is determined based on sklearn and gensim methods
• 6
  、How to read gensim

How to incrementally train TF-IDF and LDA models in Gensim?

Incremental updates of TF-IDF and LDA models in Gensim need to be achieved by expanding the corpus and re-training the models. Here are the specific steps and code examples: TF-IDF model incremental updates extended corpus adds new documents to an existing corpus. If the corpus is in list form, it can be appended directly; if you use the corpora.TextCorpus class, you need to call its extended methods (such as add_documents).

In Gensim, incremental training of TF-IDF and LDA models can be achieved by loading existing models, processing new data, and invoking specific methods, without having to retrain the entire model. The specific steps are as follows: TF-IDF model incremental training and loading existing models Use gensim.models.tfidfmodel.load () to load saved TF-IDF model files (such as tfidf_model.gensim).

Preprocess the training corpus First, install Gensim using anaconda and install Gensim through pip. The core of pre-processing is to transform the original text into sparse vectors for model understanding. This includes word segmentation, removing stop words, and transforming the document into a list of features, such as word frequencies in a bag-of-words model. The core of topic vector transformation Gensim lies in text vector transformation, which uses topic models to mine the internal structure of corpus.

Use TF-IDF (word frequency-inverse document frequency) or TextRank algorithm to extract the most important keywords from the text. These keywords should be able to represent the theme and content of the text. Select Top N keywords: Select the top N keywords according to their importance as candidates for subsequent substitutions.

Use Anaconda or pip to install, and the command is pip install gensim. Preprocessing training corpus: Purpose: To transform the original text into sparse vectors that the model can understand. Steps: Including word segmentation, removing stop words, and converting the document into a feature list. Topic vector transformation: Core: Mining the internal structure of corpus through topic models.

Please give me a transliteration of the song An Qixuan (Mask). Thank you for your help.

1. The analysis of the lyrics of the Seven Friends can be carried out. Because the song Seven Friends was sung by a certain singer, the lyrics describe the emotional entanglements and stories between the seven friends. Through the lyrics, you can feel the sincerity and firmness of friendship. The melody of the song is cheerful and pleasant, and the lyrics are also full of emotion, which aroused the resonance of the audience.

2. Song of the Seven Sons·Macau, do you know that Mamang is not my real name? I have been away from your swaddling clothes for too long, mother! But what they stole was my body, and you still kept my inner soul. That biological mother who has never forgotten for three hundred years! Please call me "Macau" by your nickname! Mother! I want to come back, mother! Song of the Seven Sons·Taiwan We are a string of pearls held by the East China Sea, Ryukyu is my brother, and I am Taiwan.

3. Dear baby, I recommend it to everyone here. Sugar - Heartful This time, a total of four Japanese and Korean women came together. It was simply too violent. The MV was very beautiful and sounded a little irreparable sadness. SNoW -Namaï Butterfly [OP track of the animation "Hell Girl"] SNoW Her voice is very suitable for listening to when you are upset. I haven't included her album. If you are interested, you can go and listen to it.

4. I found a lot of songs with rainy seasons ending. See for yourself which one you are looking for.

5、minutes 。As if telling you something, listen to RNl+7 41. westlife-my Yix（@ love
。Everyone is too familiar with it. If you haven't heard of it, you can quickly download 842. Twins-Next stop, Tianhou. Good melody] 43. twins - |Love is bigger than heaven. I love this GS! zk 44。Fan Weiqi-Can you not be brave? The lyrics are well written. It's a song with a selling point on the entire album.

Natural Language Processing-Using Word Vector (Tencent Word Vector)

1. Tencent Word Vector is a pre-trained word vector model trained by Tencent AI Lab based on a large-scale Chinese corpus. It contains more than 8 million Chinese terms, each term corresponding to a 200-dimensional vector. Its core advantage is that it captures the semantic and grammatical characteristics of words through deep learning models such as Directional Skip-Gram, which is suitable for multiple natural language processing tasks.

2. Word vector is a core concept in natural language processing. It is used to map words or phrases from a vocabulary to the real space of the vector so that a computer can understand and process it. The following is a detailed explanation of word vectors and their use in natural language processing. The concept of word vector comes from concepts such as "value" and "distribution" in linguistics.

3. The training of word vectors is usually based on neural probability language models, and word vectors are obtained by optimizing model parameters. The emergence of word vectors has greatly promoted the development of NLP, making significant progress in various natural language processing tasks.

4. Word Embedding is a technique that maps words in natural language to real vector space. Detailed interpretation of word vectors is a basic technology in Natural Language Processing (NLP). Its core goal is to transform the symbolic representation of language (i.e. words or phrases) into a mathematical vector representation. This transformation allows machines to better understand and process natural language because mathematical vectors can be easily used for calculations and analysis.

5. GloVe algorithm combines global statistical information and local context information to capture the semantic and grammatical relationships between words. Application of word vectors Word vectors have widespread applications in the field of natural language processing. The following are some common application scenarios: Text classification: Text classification is performed by converting each word in the text into word vectors and using these vectors as features.

Big data analysis sets sail

In response to the launch of big data analysis, combined with scientific research needs and Douban film review data set, we can advance according to the following frameworks: Clarify scientific research goals and core issues of data value: Focus on new social phenomena (such as online public opinion dissemination and changes in cultural consumption behavior), and use Douban film review data Explore user emotional tendencies, group behavior patterns or market feedback mechanisms. Data advantage: Scale: 540,000 records cover a three-year span, which can support long-term trend analysis.

Passive adaptation to technological change: The understanding of big data only stays on the surface phenomenon of "being surrounded by data", and does not actively explore technical principles and application scenarios. For example, I only noticed the advertising recommendation algorithm, but did not deeply understand the core technologies such as user portrait construction and collaborative filtering behind it.

Qihang's business scope covers three major areas: financial technology, technical services and education and training; its development strategy is innovation-driven as its core and achieves sustained growth through win-win cooperation, market expansion and global layout. Business Scope Financial Technology Qihang Co., Ltd. focuses on technological innovation in the financial industry and provides risk assessment and prediction services to financial institutions through tools such as intelligent risk management systems.

Today, when big data is surging like a wave,"Thousands of rivers meet and the vast sea" vividly depicts the magnificent scene of data gathering into a sea, while "the wind is good and setting sail" means that it is time to set sail and join the field of big data. Great opportunity. The following is a detailed explanation of the exploration of the field of big data and the development of the workplace: Broad prospects in the field of big data Big data is called the "oil" and "coal" resources in the new era, and its importance is self-evident.

Fleet Online HiFleet Shipping Safety and Business Decision Big Data Platform is a comprehensive shipping service platform integrating ship monitoring, data analysis, safety warning and business decision support. Its functions are summarized as follows: Global ship real-time monitoring relies on 3000 AIS base stations around the world. and 58 AIS satellites obtain 500 million ship position data every day, realizing real-time position tracking of global ships.

Smart workshops realize production automation, digitalization and visualization through technologies such as the Internet of Things, big data, and artificial intelligence. They are the core components of smart factories. Their characteristics include data-driven, autonomous control, collaborative collaboration and visual display. The advantages are reflected in improving efficiency, quality, cost reduction and enhanced competitiveness.

The number of LDA topics is determined based on sklearn and gensim methods

When selecting the best number of topics for the LDA model, confusion and consistency need to be considered comprehensively. If there is no minimum value for confusion, select the number of topics with the highest consistency; if there is no maximum value for consistency, select the number of topics with the lowest confusion. In addition, the optimal number of topics can be further determined through visualization of the LDA model and observed whether there is overlap of topics in the visualization diagram. To sum up, by combining the methods of sklearn and gensim libraries, we can more accurately determine the optimal number of topics for the LDA model.

Vectorization: Build dictionaries and bag of words models through the Gensim library to convert text into numerical vectors. The LDA topic model analyzes topic number selection: 4 to 24 topics were tested, and the optimal number of topics was selected to be 12 based on the Coherence Score. Coherence score visualization results: The score is the highest (0.6549) for 12 topics, and the model effect is the best.

Theme modeling: Use LDA (Latent Dirichlet Allocation) for theme modeling.

Method 1: TF-IDF + cosine similarity principle: Convert text into TF-IDF vectors, calculate cosine similarity between vectors, and take the average value as text similarity.

Method 2: Jensen-Shannon divergence (JSD) LDA topic model training Use the preprocessed corpus to train the LDA model and determine the number of topics $K $(which can be optimized by confusion or topic consistency scores). Each report is represented as a topic distribution vector with dimensions of $K $, and each element is the probability of the corresponding topic.

How to read gensim

1. The pronunciation of gensim (/dnsm/) is jen-sim, where g is pronounced j. The name gensim comes from Generate Similar, which means generating similar text data. This name corresponds to gensim's main functions, namely, generating text vectors, calculating text similarities, building topic models, and classifying topics.

2. nltk Chinese stop words, complete code display, contains 841 Chinese stop words. nltk English stop words, the complete code display, contains 179 English stop words. The sklearn library can also obtain stop words. Specific code and running screenshots show that sklearn contains a total of 318 English stop words. The parsing.preprocessing module of the gensim library provides stop word calling function. The running screenshot shows that gensim contains a total of 337 English stop words.

3. Use GooSeeker to perform word segmentation and obtain a word segmentation effect table as a Chinese corpus. Convert the word segmentation result data into a csv file. Use the Gensim library to convert csv files into sentences data structures. Input the sentences data structure into the word2 vec model for training. When training the model, parameters such as min_count (word frequency threshold), vector_size (word vector dimension), etc. need to be considered. This notebook will compare models trained in different dimensions.

4. Gensim Library: Supports technologies such as Word 2 Vec and FastText to generate word vectors to accelerate tasks such as sentiment analysis and text classification, and improve computing efficiency and accuracy. Sentiment analysis uses machine learning algorithms to determine the emotional tendencies of text (such as positive/negative). Commonly used library: Scikit-learn: Provides classification models (such as logistic regression, SVM). NLTK: Integrate emotion dictionaries and pre-trained models to simplify the analysis process.

5. Summary This project implements text similarity analysis based on TF-IDF and LSI through Python and gensim libraries. By reading dictionaries and documents, calculating TF-IDF and LSI vectors, generating similarity matrices, and calculating similarity, we can effectively find other documents that are most similar to a given document. This is of great significance for applications such as information retrieval, text classification and recommendation systems.