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Analyzing AI business that identifies logos and trademarks and performs a sentiment analysis

Updated: May 16

Stakeholder, Impact and ROI

Stakeholders are as follows:

1. Trademark owners are those individuals or companies responsible for protecting their intellectual property through the trademark or logo.

2. Consumers are significant parties as they make decisions based on their perception of the brand.

3. AI developers are in charge of creating the technology to recognize and sort trademarks and logos.

4. Government agencies are responsible for controlling the trademark and logo usage in order to protect consumers and prevent illegal use.

5. Legal specialists commonly participate in disputes regarding trademarks and logos and can represent the trademark owners and infringers.

Marketing professionals use trademarks and logos to create a brand identity and advertise products and services.


1. Improving customer experience can result in increased sales and services.

2. Tailoring products to target specific customer segments, as well as targeted advertising, can lead to better ROI and cost savings.

3. Using negative sentiments as a means of product improvement can provide a competitive advantage.

4. High-quality insights can help brands make more informed strategic decisions and improve profits.

Competitive advantage can be achieved to create differentiation in their product. Profit = Price * quantity – costs.


1. Managing brand reputation: The analysis enables brands to keep track of online discussions and detect any issues related to the brand, allowing appropriate corrective measures to be taken.

2. Developing products: It can highlight areas that require improvement, which can be useful for future product development.

3. Analyzing competition: The analysis allows comparison of brand perception across different brands.

4. Advertising: It can help adjust targeted advertising.

5. Enhancing customer service: It can lead to improvements in customer service in relevant areas.

Technical Roll out & Change management

Technological rollout:

1. Gathering information from multiple sources such as social media, reviews, and surveys.

2. Cleaning the text by eliminating irrelevant information. Techniques like tokenization, stop word removal, and stemming may be employed.

3. Creating and refining the algorithm to classify the text as either positive, negative, or neutral.

4. Teaching the algorithm how to classify new data by exposing it to labeled training data.

5. The algorithm is then utilized to classify new data from various sources.

Based on the results from step 5, conclusions are drawn and actions are taken accordingly.

Change management:

1. Establish clear goals and objectives so that the organization understands the purpose of conducting sentiment analysis.

2. Identify all the involved parties, also known as stakeholders.

3. Assess the stakeholders' willingness to change, which may involve examining their attitudes, behaviors, and current processes related to using sentiment analysis.

4. Create a change plan based on the steps above, which should outline specific actions, timelines, and resources required for effective sentiment analysis implementation.

5. Communicate the benefits of sentiment analysis to the stakeholders identified.

6. Deploy sentiment analysis and monitor whether the goals and objectives established in step (1) are being met.

To achieve the maximum impact of trademark and logo sentiment analysis, continuous improvement is essential. This may entail gathering feedback, analyzing the results, and adjusting the technology, procedures, and training as needed.

Societal Concerns

Bias & Fairness:

1. The algorithms might not have the relevant context for the use of sentiment analysis leading to unfair assessment of sentiments.

2. The algorithms might not account for cultural or demographic differences which might lead to different interpretation of certain trademarks or symbols.

3. Training data to train the AI might be biased or imbalanced.

4. Human bias might be present in the training data which might lead to biased results. Such as the way the algorithm is trained might erroneously implied blue skyà negative sentiments.


1. What factors are being considered to arrive at result?

2. Is sometime which is flagged a company based issue or industry wide issue?

Accountability & Trustworthiness:

1. Data security and privacy: It's crucial to make sure that any data used for sentiment analysis is gathered and stored safely, as well as that people's privacy is safeguarded.

2. Data quality : The quality of the data that sentiment analysis is trained on is critical. It is crucial to employ high-quality, representative data that is impartial in order to assure accountability and dependability.

3. Transparency of algorithm is crucial to determine the factors behind the conclusion.

4. Evaluation and validation tests are imperative to conclude that the model is relevant and free from bias.

Human oversight is imperative to address any potential biases or error in the results.


1. Some false positives can be potentially accepted because the case is complicated.

2. The biasness towards other brands is addressed.

3. Misclassification: The algorithm may wrongly classify the sentiment of a trademark or logo, which could lead to incorrect conclusions and actions.

If the sentiment analysis software or data is not appropriately secured, there is a risk of hacking or cyberattacks.

Liability Measures

Liability Concerns

1. Discrimination could be problematic and result in biased treatment of particular communities, which would violate the principle of equality and lead to potential legal consequences.

2. Defamation/Libel: Brands may object if their sentiment analysis results are incorrect and made public.

3. Data protection regulations and privacy policies must be observed to ensure the algorithm generates accurate results.

4. When conducting sentiment analysis, it's important to comply with the platform's privacy policy and terms of use to avoid any violations.

Misinterpretation: If the sentiment analysis results are misconstrued or misused, there may be legal liability for deception or breach of contract.

Liability of various stakeholders:

1. Trademark owners: Defamatory, unfair competition, misleading advertising, trademark infringement, and trademark dilution are all legal offences for which trademark owners may be held accountable. Trademark owners should make sure their trademarks are not used in any unfavourable or destructive ways to protect themselves from liability. Additionally, trademark owners need to make sure that sentiment analysis performed on their trademarks or logos is accurate and not based on erroneous assumptions. Owners should not use sentiment analysis to sway public opinion or target particular demographics with their messaging; rather, they should only use it to better understand their brand.

2. Consumers: Understanding that a consumer's right to voice their opinion is not unlimited is essential. Consumers should be informed that their opinion can be viewed as inaccurate or misleading and that they might face legal repercussions as a result.

3. AI developers: Algorithmic bias and inaccurate results might lead to legal repercussions. As a result, it is forbidden to utilise AI-based sentiment analysis to spread untrue information about a company's goods, services, or reputation. AI developers must take care to respect both any applicable copyright laws as well as the rights of owners of trademarks and logos. Additionally, sentiment analysis based on AI must be developed with future defamation claims in mind. They may be held responsible for any damages that ensue if their AI technology is employed in a manner that generates false and negative remarks about a business or brand. Developers must be careful to distinguish between opinion and reality when creating AI-based sentiment analysis of trademarks and logos in order to ensure accuracy and fairness.

4. Government agencies: Governmental organisations must make sure that trademark and logo sentiment analysis is done in a fair, honest, and legal way. This may entail ensuring that sentiment analysis be carried out in compliance with (privacy) laws and regulations, that the data used for the analysis be accurate and reliable, and that the analysis be carried out in a way that is devoid of prejudice or manipulation. Governmental organisations should also take action to inform businesses and the general public on how sentiment analysis should be used in relation to trademarks and logos. This includes informing the general audience of the value of accuracy and dependability in the analysis.

5. Marketing professionals: Before starting a campaign, they should be sure to speak with legal counsel and have the necessary approval from the concerned organisation. Additionally, they should make sure that any sentiment analysis data is reliable and current and that any conclusions or claims about a brand are not misrepresentative. Additionally, before utilising sentiment analysis, marketers should get the consent of any parties who may be impacted by it and should consider any cultural or religious ramifications.

Social media platforms: IPR violations are common practice and intermediary protections and safe harbour provisions are limited under various laws. This involves alerting the user to potential infractions, deleting or restricting the content, and giving them the tools they need to appropriately licence the work. In addition to protecting the integrity of the questioned trademark or logo, this can help avoid potential liabilities. Platforms should also make sure that sentiment analysis of trademarks and logos is expressly forbidden in their terms of service. In the event that such infractions take place, this can offer legal remedies. Additionally, platforms must to have a clear policy allowing users to report any sentiment analysis of trademarks and logos that they feel to be an infringement of their intellectual property rights.

Awareness and Safeguards:

1. Ensure that algorithmic bias is eliminated and that the reasoning behind a particular conclusion is understandable.

2. Identify and address bias at all stages.

3. Make stakeholders aware of their responsibilities.

4. Provide users with warnings about potential violations, removing or limiting access to content, and giving them the necessary tools to license the content properly. This not only protects the integrity of the trademark or logo in question but also helps prevent potential legal issues. Furthermore, platforms should explicitly prohibit sentiment analysis of trademarks and logos in their terms of service, which can provide legal remedies in the event of any violations. Platforms should also have a clear policy allowing users to report any sentiment analysis of trademarks and logos that they believe to be an infringement of their intellectual property rights.

Bias must be considered when conducting sentiment analysis of trademarks and logos. For example, sentiment analysis often employs web-based datasets that may be skewed based on audience, region, and other factors. Therefore, it is critical to ensure that the dataset is comprehensive and representative.

By Siddharth Dalmia

The StartUp Sherpa


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