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It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
521
aziende
Abbiamo dati su 521 aziende che usano Gensim. La nostra lista di clienti Gensim è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
![]() Icertis | Stati Uniti | Software Development | 2.3K | $150M |
Praxis Engineering | Stati Uniti | Software Development | 310 | $13M |
Databricks | Stati Uniti | Software Development | 8.8K | $600M |
A3 Missions LLC` | Stati Uniti | It Services And It Consulting | 51 | |
Home Depot / THD | Stati Uniti | Retail Furniture And Home Furnishings | 491K | $157B |
The Home Depot | Stati Uniti | Retail | 491K | $157B |
Integrant | Australia | Research Services | 3 | |
![]() RELX | Regno Unito | Technology, Information And Media | 2.5K | $10B |
Leidos | Stati Uniti | It Services And It Consulting | 33K | $14B |
Lilly | Stati Uniti | Pharmaceutical Manufacturing | 41K | $29B |
LexisNexis | Stati Uniti | It Services And It Consulting | 11K | $3.2B |
Illumination Works LLC | Stati Uniti | It Services And It Consulting | 68 | $32M |
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Gensim è utilizzata in 27 paesi
Ci sono 20 alternative a Gensim
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Gensim is a popular open-source Python library designed for unsupervised topic modeling and natural language processing tasks. It provides an easy-to-use interface for various statistical semantics, document similarity analysis, text summarization, and more. Gensim stands out for its efficiency in handling large text datasets and its scalability in processing and analyzing textual data.
Gensim falls under the category of NLP (Natural Language Processing) and sentiment analysis tools. NLP involves the interaction between computers and human language, enabling machines to understand, interpret, and generate human language. Sentiment analysis, on the other hand, focuses on identifying and categorizing opinions expressed in text data to determine the sentiment conveyed, such as positive, negative, or neutral.
Founded in 2009 by Radim Řehůřek, Gensim stemmed from the motivation to provide a simple and efficient solution for processing and analyzing textual data for various NLP tasks. Over the years, Gensim has gained significant traction in the AI community due to its robust capabilities and ease of use.
Currently, Gensim holds a notable market share within the NLP and sentiment analysis category, being widely adopted by researchers, data scientists, and industry professionals. With the increasing demand for advanced NLP applications in various industries like finance, healthcare, and marketing, Gensim is poised to experience further growth in the future. Its continuous development and adaptability to evolving NLP trends make it a competitive choice for those seeking powerful text processing tools.
Gensim is a powerful tool that companies use to leverage Natural Language Processing (NLP) and Sentiment Analysis capabilities for various applications. Its user-friendly interface and robust features make it a top choice for organizations looking to extract valuable insights from textual data.
Gensim excels in efficient topic modeling, making it easier to discover underlying patterns within large volumes of text data. Its scalability and speed set it apart from other similar technologies, ensuring quick and accurate results even with extensive datasets.
One of Gensim's key strengths lies in its ability to calculate document similarity with precision. Its versatile algorithms offer a comprehensive approach to measuring similarity, allowing companies to perform detailed comparisons effortlessly. This flexibility outshines competitors by providing nuanced results tailored to specific business needs.
Gensim incorporates state-of-the-art word vectorization techniques, enabling users to generate high-quality word embeddings for diverse NLP tasks. By utilizing cutting-edge methodologies, Gensim surpasses traditional text analysis tools, delivering accurate and contextually rich representations of words and phrases.
Gensim is designed with scalability and performance optimization in mind, ensuring seamless processing of extensive text corpora. Its efficient memory management and parallel processing capabilities make it a preferred choice for companies handling large-scale NLP projects, outperforming competitors in terms of speed and resource utilization.
By harnessing the benefits of Gensim, companies can unlock the full potential of NLP and Sentiment Analysis, gaining a competitive edge through data-driven decision-making and enhanced customer insights.
Gensim, a popular natural language processing (NLP) and sentiment analysis library, is utilized by various renowned companies to extract valuable insights from textual data. Let's explore some insightful case studies showcasing how these companies leverage Gensim for enhancing their operations and decision-making processes.
1. Airbnb Airbnb, a leading online marketplace for lodging and travel experiences, utilizes Gensim for text analysis and clustering of user reviews. By implementing Gensim, Airbnb can categorize reviews based on sentiments, identify trends, and improve customer experience. The company started using Gensim in 2017 to automate the analysis of millions of user-generated reviews efficiently.
2. Spotify Spotify, one of the largest music streaming platforms globally, leverages Gensim for enhancing its recommendation algorithms. By utilizing Gensim's capabilities in semantic analysis, Spotify can understand user preferences better and personalize music recommendations. Spotify integrated Gensim into its system back in 2015, leading to a significant improvement in user engagement and satisfaction.
3. Yelp Yelp, a popular platform for discovering local businesses and services, employs Gensim for sentiment analysis of customer reviews. By utilizing Gensim's text processing functionalities, Yelp can extract insights on customer sentiments and preferences, enabling businesses to improve their offerings. Yelp has been using Gensim since 2016 to enhance the quality of its review analysis and recommendation systems.
These case studies highlight the diverse applications of Gensim in real-world scenarios, showcasing its effectiveness in NLP and sentiment analysis for companies across different industries.
Puoi accedere a un elenco aggiornato di aziende che utilizzano Gensim visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 521 aziende che utilizzano Gensim.
Gensim è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "Software Development", "Software Development", "Software Development", "It Services And It Consulting", "Retail Furniture And Home Furnishings", "Retail", "Research Services", "Technology, Information And Media", "It Services And It Consulting", "Pharmaceutical Manufacturing". Per un elenco completo di tutti i settori che utilizzano Gensim, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano Gensim includono Icertis, Praxis Engineering, Databricks, A3 Missions LLC`, Home Depot / THD, The Home Depot, Integrant, RELX, Leidos, Lilly e molte altre. Puoi trovare un elenco completo di 521 aziende che utilizzano Gensim su TheirStack.com.
Secondo i nostri dati, Gensim è più popolare in Stati Uniti (229 companies), India (31 companies), Regno Unito (31 companies), Francia (20 companies), Spagna (10 companies), Germania (9 companies), Canada (7 companies), Australia (6 companies), Paesi Bassi (6 companies), Irlanda (5 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano Gensim cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.