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The Challenge

• Diverse NGO Services: NGOs offer a myriad of services ranging from medical assessments, legal aid, language instruction, to programs related to gender-based violence. Understanding the breadth and specificity of these services was a challenge.
• Language Barriers: With programs offered in multiple languages like English,French, and Russian, it was essential to ensure the tool could cater to various linguistic groups.
• Effective Matching: Individuals seeking support often struggle to find the right NGO program, particularly if they lack resources. It was crucial to develop a tool that could accurately match a person’s needs with the right service.
• Data Compilation: With vast amounts of data scattered across different NGO websites, the client faced the challenge of extracting, compiling, and presenting this information in a user-friendly manner.

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The Process

• Data Extraction: The client’s tool was designed to crawl various NGO websites and extract pertinent information about the diverse programs they offer.
• Algorithm Development: An advanced matching algorithm was developed to efficiently pair individuals with suitable NGO programs based on their profiles.
• Feedback Loop: The tool incorporated a feedback mechanism to continually refine its matching process, ensuring greater accuracy over time.

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The Output

• Comprehensive Database: The tool successfully compiled a vast database of NGO programs, categorized by service type, language, eligibility criteria, and more.
• Efficient Matching: Individuals in need could now find the most suitable NGO programs in mere seconds, ensuring they receive the assistance they require.
• Community Benefits: By connecting individuals to free or low-cost programs, the tool ensured that more people could access essential services, leading to stronger, more resilient communities.
• Lead Generation: The tool also served as a lead generation platform, offering the compiled data at affordable rates for various stakeholders in the NGO sector.

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Conclusion

Our client’s innovative tool successfully addressed a significant gap in the NGO sector by efficiently connecting individuals in need with the right resources. By leveraging technology, the tool not only streamlined the process of finding appropriate NGO programs but also created a platform that could evolve and adapt based on feedback and changing societal needs. This case study underscores the immense potential of digital solutions in addressing complex societal challenges and paves the way for more such innovations in the future.

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The Challenge

Our client faced a significant hurdle in extracting valuable leads from vast amounts of multilingual data that they generate regularly. To overcome this challenge, they approached us with the need for a tool that could efficiently translate content from different languages, identify key entities, and then re-translate them into their original language for verification.

The Process

Our solution involved a comprehensive process that  seamlessly integrated with the client’s workflow:

1. Translation and Entity Extraction: The tool efficiently translated content from various languages into English, preserving the original meaning. It also systematically identified key entities from the data, making it highly
   adaptable.
2. Noun Extraction in English: Following translation, the tool systematically identified nouns in the English data. This step was crucial in extracting names and company information from the content.
3. Translation back to original language for Verification: The extracted
   names and company details were then translated back into it’s original language. This step served to verify the accuracy of the information in the original context.
4. Customization for Multilingual and Varied Data: The versatility of the
   tool was a key feature. It could be customized to function with any language, allowing the client to adapt it to various markets. Furthermore, the tool seamlessly processed data in different formats, providing flexibility in its application.
5. Information Extraction: Once verified, the tool efficiently extracted valuable information, including leads, from the processed data. This step ensured that the client could gather meaningful insights and potential business opportunities.

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Output

The output of our tool was twofold. Firstly, it successfully addressed the client’s immediate need by providing an efficient means of lead generation from multilingual data. Secondly, the tool’s customization feature opened up possibilities for its application in diverse linguistic and data environments, offering the client a scalable and adaptable solution for future challenges.

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Conclusion

In conclusion, our tailored tool not only met the client’s specific requirement for lead generation from multilingual data but also demonstrated its potential for roader applications. By leveraging systematic entity extraction and versatile language translation, we created a powerful tool that empowers our client to unlock valuable insights from a wide range of multilingual and varied data sources. This case study serves as a testament to our commitment to providing innovative solutions that align with our client’s evolving needs.

The Challenge

The challenge lay in the intricacies of LinkedIn’s security measures. LinkedIn is renowned for its stringent security protocols, akin to other prominent social media platforms like Facebook and Instagram. These platforms make scraping data from their backend APIs a formidable task. They employ a multitude of security checks and obstacles to prevent automated data extraction.

Additionally, the client had a specific set of requirements that included capturing data on the most recent posts from the target profile. This entailed recording critical details such as the post’s URL, date of post, the number of likes and reactions it received, the total number of comments, and the post’s caption. However, the client did not require the retrieval of images included in the posts. Furthermore, the tool needed to be capable of extracting data from the selected profile efficiently and quickly.

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While images were not included, this streamlined approach allowed for efficient and quick data
extraction. The tool operated seamlessly, collecting data from LinkedIn profiles for up to one year in a single run. This meant that users could access a year’s worth of posts from any profile, providing valuable insights for data analysis and sentiment assessment.

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Conclusion

Our client presented us with a distinctive challenge: to scrape LinkedIn posts from a specific profile spanning a year. Despite LinkedIn’s robust security measures, we successfully developed a custom scraping tool that efficiently navigated the platform’s backend API calls. By mimicking human behavior and employing login cookies, we ensured the tool’s effectiveness and compliance with the platform’s security checks. The output of our tool met the client’s requirements precisely. It provided a dataset containing essential post details, enabling data analysis and sentiment assessment. This case study showcases our ability to tackle complex scraping tasks, even on highly secured platforms, and deliver efficient, customized solutions to meet our client’s unique needs.

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The Process

Our approach involved the development of a custom scraping tool from scratch. This tool was designed to effectively navigate LinkedIn’s intricate backend API calls. It utilized login cookies for authentication, enabling it to access profiles and collect data.

The tool’s operation was based on the concept of mimicking human behavior, ensuring that its scraping activity appeared as genuine as possible to the platform’s security measures. This
approach enabled the tool to access and extract the required data without arousing suspicion.

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The Output

The output of our custom scraping tool was exactly aligned with the client’s requirements. For each post within the specified profile, the tool collected and compiled data.
This dataset included details such as the post’s publication date, its URL, the total number of likes and specific reactions (including empathy, praise, interest, entertainment, and appreciation), the total number of comments, and the post’s caption.

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