• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Housing Technology Main Logo

Housing Technology

Housing | IT | Telecoms | Business | Ecology

  • Free Subscription
  • Contact
  • Home
  • Research
  • Magazine
  • Events
  • Awards
  • Recruitment
  • On Demand
Home / Free Subscriber Access / Sentiment analysis & TSMs

Sentiment analysis & TSMs

Sentiment analysis, also known as opinion mining, has a history dating back to the 1960s when psychologist Stuart Oskamp made the first attempt to classify text based on sentiment using a simple counting method.

In the 1980s and 1990s, sentiment analysis gained popularity in market research, then in the early 2000s, machine-learning algorithms were introduced, allowing for more accurate analysis of large datasets. With the emergence of social media in the mid-2000s, sentiment analysis became an important tool for monitoring and understanding public opinion on platforms such as Twitter and Facebook.

In recent years, sentiment analysis has become more widely-used across a variety of sectors, including finance, healthcare and politics. Advances in natural language processing (NLP) and machine learning have led to more accurate sentiment analysis and the ability to analyse more complex types of text, such as social media posts and product reviews. Today, sentiment analysis is a rapidly-growing field and continues to be an important tool for understanding and analysing public opinion.

Uncovering satisfaction

With the tenant satisfaction measures (TSMs) coming into effect in April 2023, understanding the sentiment of tenants will be more important than ever. While the survey questions themselves have fixed responses, the verbatim comments could be a valuable source of additional data for housing providers on the reasons behind their feedback, enabling data-led strategies on what improvements and changes need to be made.

Advantages

Scalability – Sentiment analysis can be applied to large volumes of text data, making it useful for processing not only verbatim feedback but also social media feeds and other forms of user-generated content at scale.

Speed and efficiency – Sentiment analysis algorithms can quickly and accurately classify the sentiment of a large volume of text, saving time and effort compared with manual analysis.

Insights – Sentiment analysis can provide valuable insights into customers’ opinions, helping housing providers make data-driven decisions.

Disadvantages

Accuracy – Sentiment analysis algorithms are not always accurate and may misclassify sentiment, especially in cases of sarcasm or irony.

Contextual limitations – Sentiment analysis algorithms often struggle to account for the nuances of language and sector-specific context, leading to errors in classification.

Bias – Sentiment analysis algorithms may be biased towards certain sentiments or groups of people, depending on the training data used.

Lack of granularity – Sentiment analysis can only provide a high-level analysis of sentiment and may not capture the nuances of sentiment within a piece of text.

Emotion vs. sentiment

Sentiment and emotion are related concepts but they are not the same thing. Understanding both emotions and sentiments in natural language processing (NLP) is important because they provide different and complementary information.

Sentiment refers to the polarity of a text, whether it is positive, negative or neutral. Sentiment analysis is useful for quickly gaining a general understanding of the attitude expressed in a piece of text.

Emotion, on the other hand, refers to the feelings or states of mind evoked by a text, such as anger, joy, fear or sadness. Emotion analysis is more nuanced than sentiment analysis because it attempts to capture the complex and multi-dimensional nature of human emotions.

Understanding emotions in addition to the sentiment can provide deeper understandings and insights into the impact an experience had or is having on a customer.

The future of sentiment analysis in housing

Multi-lingual sentiment analysis – With such a growing, diverse cultural composition across UK housing providers, multi-lingual sentiment analysis has become more important. Advanced machine-learning models that can accurately analyse sentiment in multiple languages will be in high demand.

Real-time sentiment analysis – The ability to analyse sentiment in real-time, integrated with chatbots, voice assistants and recommendation engines, providing support to both agents and customers.

Emotion detection – Sentiment analysis has moved beyond simply identifying positive or negative sentiment to detecting emotions such as anger, joy or sadness. This will enable businesses to provide more personalised customer experiences and make more informed business decisions.

Caroline Thomas is a senior CX service designer at FourNet.

See More On:

  • Vendor: FourNet
  • Topic: Customer Management
  • Publication Date: 092 – March 2023
  • Type: Contributed Articles

Primary Sidebar

Most Recent Articles

  • Artificial intelligence in housing
  • Mobysoft – Data problems affecting complaints’ handling
  • Data, AI and private-sector strategies
  • Smart repairs & smarter homes
  • From firewalls to fortresses
  • Achieving three quick wins in AI
  • Rebuilding Selwood Housing’s IT infrastructure
  • Are you ready for organisational AI?
  • PIMSS releases AI Document Reader for compliance
  • Calico Homes cuts arrears with RentSense
  • FourNet launches digital transformation index
  • New income recovery software from Voicescape
  • Asprey Assets at YMCA
  • I love spreadsheets…
  • All watched over by machines of loving grace – AI assistants and adult social care
  • The rent revolution – The case for AI-powered payments
  • Unlocking safer living through data
  • Aareon acquires MIS ActiveH
  • Vericon launches MouldSense
  • Back to the future at Housing Technology 2025
  • FireAngel wins Which? Award
  • Maximising income and preventing homelessness
  • Anchoring digital innovation with Plentific
  • Cynon Taf Community Housing gets Housing Insight’s Arrears Manager
  • Tenants, AI & your biggest compliance risk
  • EDITOR’S NOTES – Data, standards & straight-through processing
  • AI as a social housing expert
  • South Yorkshire Housing halves arrears with Mobysoft
  • Bromford Flagship wins Aico’s smart-home competition
  • Putting VIVID’s customers in control of their tenancies

Footer

Housing Technology Main Logo
  • Instagram
  • LinkedIn
  • YouTube
  • Contact
  • Free Subscription
  • Book an event
  • Research
  • Update Your Subscription
  • Privacy Policy

Welcome to the housing Technology – Trusted Information For Business Professionals in HOusing

Housing Technology is the leading technology information service for the UK housing sector and local governments. We have always believed in the fundamental importance of how the UK’s social housing providers use technology to improve their tenants’ lives.

Subscribe to Housing Technology to gain market-leading research, unsurpassed peer networking opportunities and a greater understanding of your role to transform your business.

Copyright © The Intelligent Business Company 2025 | Terms and Conditions | Privacy Policy
Housing Technology is published by the The Intelligent Business Company. A company with limited liability. Registered in England No. 4958057 | Vat Registion No. 833 0069 55.

Registered Business Address: Hoppingwood Farm, Robin Hood Way, London, SW20 0AB | Telephone: +44 (0) 20 8336 2293