International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:04|Apr2025 www.irjet.net p-ISSN:2395-0072
Automated Research and Trigger Finder
NexusAD
Dr .Nita Patil , Ganesh Dubey , Aishwarya Mishra , Swaroop Morajkar
1HOD, Dept. of Computer Engineering, K.C. College of Engineering and Management Studies and Research, Maharashtra, India
2Student, Dept. of Computer Engineering, K.C. College of Engineering and Management Studies and Research, Maharashtra, India
Abstract: NexusAdisanAI-poweredmarketingintelligence platform designed to simplify and accelerate market research. It aggregates data from various sources and delivers real-time insights through intuitive visualizations. The platform focuses on pre-campaign analysis, enabling smarter and faster marketing decisions. By integrating AIdriven sentiment analysis and competitor benchmarking, NexusAd offers a comprehensive tool for data-driven strategy building. Its user-friendly design ensures accessibilityforbothprofessionalsandbeginners.
I.INTRODUCTION :
Intoday’sfast-paceddigital world, marketing hasevolved intoadata-drivenfield.Organizations,especiallystartups, facechallengesinprocessingvastamountsofinformation and gaining actionable insights from scattered sources. Traditional tools and research methods are often expensive, time-consuming, and limited in predictive capabilities.Thisleadstodelayeddecisionsandineffective strategies. NexusAd aimstobridgethisgapbyofferingan AI-powered platform for marketing intelligence that automates research, trend detection, sentiment analysis, andcompetitortracking.Byaggregatingdatafromvarious sources and providing real-time, easy-to-understand visual insights, NexusAd empowers marketers to make faster, smarter, and more strategic decisions without relyingonexpensiveorcomplextools.
II. PROBLEM STATEMENT :
Marketers today face numerous challenges in planning andexecutingeffective,data-drivencampaigns.Oneofthe primary issues is the fragmentation of data across multipleplatformssuchassocialmedia,searchengines,ecommercesites,andforums,makingitdifficulttogathera unified view of market behavior and customer sentiment. This disjointed data environment hinders timely and informed decision-making. Many existing marketing platforms also lack AI-powered capabilities that could automate insights, predict trends, or detect opportunities proactively. As a result, marketers are left to rely on gut feelings or limited data samples, which can lead to ineffectivestrategies,missedopportunities,increased costs, and reduced return on investment. In today’s
competitive landscape, there is a critical need for an intelligent, affordable, and easy-to-use solution that can centralize data, analyze it in real time, and generate actionableinsightstoguidesmartermarketingdecisions.
III. ADVANTAGES:
● Real-Time Insights – Provides up-to-date market trends and customer sentiment to support timely decision-making.
● Visual Representation – Easy-to-understand charts and graphs simplify complex data for betteranalysis.
● BrandComparison–Enablesdirectcomparisonof market share across brands like Apple, Samsung, andothers.
● Consumer Perception – Highlights positive, neutral, and negative sentiments to gauge brand reputation.
● StrategicPlanning–Assistsbusinessesinrefining marketing strategies based on actual market feedback.
IV. LITERATURE SURVEY:
1. Liu (2012): Discussed the fundamentals of sentimentanalysisand opinionminingusingNLP techniques. Highlighted the importance of extracting subjective information from textual data.
2. Medhat et al. (2014): Presented a detailed comparison between machine learning and lexicon-based sentiment analysis methods. Identified performance differences and applicationuse-cases.
3. Feldman (2013): Focused on real-world applicationsofsentimentanalysisinbusinessand marketing. Emphasized its role in decisionmakingandextractingcustomerinsights.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:04|Apr2025 www.irjet.net p-ISSN:2395-0072
4. Tripathy et al. (2016): Applied machine learning models (SVM, Naïve Bayes, Decision Tree) for sentiment classification. Found that the SVM modelachievedthehighestaccuracyforanalyzing customerreviews.
V. METHODOLOGY:
Fig-1:MethodologyDiagram
The methodology diagram represents the systematic approach adopted in the development of NexusAd, an AIpoweredmarketingintelligenceplatform.Thearchitecture follows a layered design pattern that facilitates modular development,scalability,andmaintainability.
1. FrontendLayer-
The Frontend Layer, built using React.js and Vue, comprises three main components: User Interface for interaction, State Management for data handling, and Visualizationforpresentinginsights.Thislayerensuresan intuitive user experience while managing complex data representations.
2. ServiceLayer-
The Service Layer acts as the intermediary between the frontend and external data sources. It contains three essential services: Search Service for query processing, MetaDataServiceforcontentcategorization,andAnalytics Service that processes marketing metrics. This separation of concerns allows for independent scaling of services basedondemand.
3. ExternalAPIServices-
The External API Services layer interfaces with multiple data sources including Google API, YouTube API, Reddit API, and various social media platforms. This diversified
approach ensures comprehensive market coverage and reducesdependencyonanysingledataprovider.
4. AIProcessingLayer-
The AI Processing Layer forms the analytical core of the platform, consisting of a Data Pipeline for preprocessing and normalization, AI Processing for applying machine learning algorithms, and Insights Generation for transforming raw analysis into actionable marketing intelligence. The bidirectional flow of information (indicated by the feedback loop) enables continuous learning and refinement of results based on user interactions.
This architectural methodology enables NexusAd to efficientlyaggregate,process,andanalyzevastamountsof marketing data across platforms, delivering cohesive insightsthatwouldotherwiserequiremultiplespecialized toolsandsignificantmanualanalysis.
VI .FIGURE:
1) Landing Page
Figure2: LandingPage
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:04|Apr2025 www.irjet.net p-ISSN:2395-0072
This screen showcases how users can securely connect their crypto wallet to access premium features, safeguard their data, and participate in the NexusAd rewards program
3) QuerySearch
Figure4:QuerySearch
The NexusAd platform features an intuitive search interface where users can enter any keyword to instantly generatecomprehensivemarketanalysis.Thissingleentry point unlocks powerful AI-driven insights across multiple platforms, delivering actionable intelligence without complexquerybuilding
4) YoutubeSentimentAnalysis
Figure5:YoutubeSentimentAnalysis
YouTubeSentimentAnalysisidentifiestop-performingads for your search query, providing detailed breakdowns of successful creative styles and key engagement metrics. This feature delivers actionable competitive intelligence byanalyzingbothindustryleadersanddirectcompetitors, enabling data-driven decisions for your video marketing strategy.
5) GoogleAnalysis
Figure6:GoogleAnalysis
Google Analysis delivers comprehensive competitive intelligence by revealing competitor strengths, weaknesses, and marketing strategies based on your search query. This powerful feature synthesizes complex market data into actionable insights and conclusions, giving you strategic advantage without extensive manual research
6) RedditAnalysis
Figure 7: Reddit Community Insights
HighlightstrendingReddit discussions,keyuser opinions, and emotional sentiment related to your query offering real-timeinsightsintopublicperceptionandproductbuzz.
7) MarketSentimentAnalysis
Figure8:MarketSentimentAnalysis
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:04|Apr2025 www.irjet.net p-ISSN:2395-0072
Provides insights into public perception by analyzing consumer opinions, identifying key sentiment drivers, and guidingstrategicdecision-making.
8) MarketDashboard
TheMarketAnalysisDashboardprovideskeyinsightsinto mobile phone trends, showcasing growth, sentiment, and brand share over time. It helps marketers make datadriven decisions through interactive visualizations of marketperformanceandconsumerperception.
VII. FUTURE SCOPE:
The NexusAd platform has strong potential for future enhancements and scalability. One key area is the integration of additional data sources such as Instagram, LinkedIn to widen market coverage. The system can also evolve by adopting real-time sentiment streaming using toolslikeApacheKafkaandimprovingtheaccuracyofAIgeneratedinsightsthroughdeeplearningmodels.
In terms of performance, cloud-based scaling can ensure system reliability during high traffic. Additionally, integrating voice-based search and insights delivery, predictive triggers, and automated campaign suggestions can take the platform closer to a fully autonomous marketingassistant.
VIII. CONCLUSION:
The development of NexusAd – an AI-Powered Marketing Intelligence Platform demonstrates how modern technologies can solve real-world challenges faced by startups and enterprises in the marketing domain. By integrating multiple APIs, AI-driven analysis, and advanced data visualization, the system effectively automates research, identifies market triggers, and generatesvaluableinsights.
The project successfully meets its objectives by offering a scalable, fast, and user-friendly solution that reduces manual effort and enhances decision-making. Through features like MetaMask integration, 3D interfaces, and
real-time data processing, NexusAd provides a practical andinnovativeapproachtodata-drivenmarketing
IX. ACKNOWLEDGEMENT:
Wewouldliketoexpressoursinceregratitudeto Dr.Nita Patil , our guide, for her constant support, valuable guidance,andencouragementthroughouttheproject.Her insights and timely suggestions played a key role in shaping our work. We are also thankful to Dr. Vilas Nitnaware, Principal,forproviding us withthe necessary resources and a supportive environment that enabled us tocompletethisprojectsuccessfully.Theirmotivationand trustinspiredus towork withdedicationandachieveour goals. We truly appreciate their contributions in making thisprojectavaluablelearningexperience.
X. REFEREFENCES:
1. LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models
Rouzrokh, P., & Shariatnia, M. (2025). This paper introduces LatteReview, a Python-based framework leveraging large language models and multi-agent systems to automate key elements of the systematic review process. Available at: arXiv:2501.05468
2. LargeLanguageModelsStreamlineAutomated Systematic Review: A Preliminary Study Chen,X.,&Zhang,X.(2025).Thisstudyevaluates theperformanceofstate-of-the-artlargelanguage models in conducting systematic review tasks, demonstrating their potential in automating such processes. Available at: arXiv:2502.15702
3. Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: A Systematic Literature Review Sundaram, G., & Berleant, D. (2022). This review identifies objectives of automation studies in systematic literature reviews and discusses the various machine learning techniques used. Available at: arXiv:2211.15397
4. AnOpen-SourceIntegratedFrameworkforthe Automation of Citation Collection and Screening in Systematic Reviews D'Ambrosio, A., Grundmann, H., & Donker, T. (2022). This paper introduces a framework that significantly reduces time and workload for collecting and screening scientific literature. Available at: arXiv:2202.10033
5. Systematic Review Automation Technologies Marshall, C., & Wallace, B. (2014). This article discusses the potential of automation in
Figure9:MarketAnalysisDashboard
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume:12Issue:04|Apr2025 www.irjet.net p-ISSN:2395-0072
systematic reviews, highlighting tools that can expedite the review process. Available at: Systematic Reviews Journal
6. TriggerTool–BasedAutomatedAdverseEvent Detection in Electronic Health Records: Systematic Review
Musy, S. N., et al. (2018). This systematic review describes current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. Available at: PubMed
7. IHIGlobalTriggerToolforMeasuringAdverse Events Institute for Healthcare Improvement. This resource provides a methodology for detecting harm in hospitalized patients using trigger tools. Available at: IHI
8. Triggers and Trigger Tools - AHRQ PSNet AgencyforHealthcareResearchandQuality.This primer discusses the use of trigger tools in detecting adverse events in healthcare settings. Available at: AHRQ PSNet
9. Evaluation of Global Trigger Tool as a MedicationSafety ToolforDetectingAdverse Drug Events
Hakkarainen, K. M., et al. (2023). This study analyzes adverse drug events identified using the Global Trigger Tool in a Finnish tertiary hospital. Availableat:Springer