Sentiment analysis now has a key role to play in key decision making for destinations in the travel and tourism industry. Understanding the sentiments expressed by tourists and travellers towards destinations can provide valuable insights for businesses, tourism boards, and even individual travellers. Here are a few ways that sentiment analysis can be useful in analysing visitor experiences to a Destination.

• Reputation Management: Sentiment analysis allows tourism boards, hotels, and travel agencies to monitor and manage the online reputation of specific key sectors and Destinations. By analysing reviews, social media posts, and online discussions, DMO and Tourism Boards can gain a deeper understanding of the positive and negative aspects associated with a destination. This information can be used to address issues, improve services, and enhance the overall visitor experience.
• Customer Feedback and Reviews: Sentiment analysis helps identify trends and sentiments in customer reviews, enabling DMOs and Tourism Boards to identify areas for improvement or strengths to highlight. It allows destinations to evaluate the satisfaction levels of visitors, understand their preferences, and tailor marketing strategies accordingly.
• Destination Marketing: By analysing social media conversations and sentiment, tourism boards can gain insights into the perception and popularity of their destinations. This information can be used to target specific demographics, create tailored marketing campaigns, and highlight unique aspects that resonate positively with potential visitors.
• Crisis Management: In the event of a crisis or negative event impacting a destination, sentiment analysis can play a crucial role in assessing the public sentiment and managing the situation effectively. By tracking and analysing sentiment in real-time, authorities and organisations can respond promptly, address concerns, and mitigate potential reputational damage.
• Competitive Analysis: Sentiment analysis allows destinations to compare and benchmark their performance against competitors. By understanding the sentiments associated with different destinations, DMOs and Tourism Boards can identify gaps, strengths, and opportunities for differentiation, which can inform their strategic planning and marketing efforts.

Sentiment for the Travel & Tourism sector has now reached a maturity that it is now an integrated data point for any DMO or Tourism Board. Sentiment analysis When applied to destinations in the travel and tourism industry offers powerful tools for understanding visitor sentiments, managing reputations, and making informed decisions. By leveraging sentiment analysis, businesses and organisations can enhance the visitor experience, develop effective marketing strategies, and ultimately drive tourism growth.

In the realm of Artificial Intelligence (AI), one of the most fascinating and impactful fields is Natural Language Processing (NLP). NLP bridges the gap between human language and machines, enabling computers to understand, interpret, and generate human language. This technology has revolutionised the way we interact with computers, making it easier for us to communicate, analyse, and derive insights from vast amounts of textual data. In this blog, we will delve into the world of NLP and explore its applications, challenges, and potential.

At its core, NLP focuses on the interaction between computers and human language, encompassing both understanding and generating natural language. The field encompasses a range of techniques, including computational linguistics, machine learning, and deep learning, to enable machines to comprehend and process textual data. NLP algorithms are designed to extract meaning, sentiment, context, and intent from written or spoken words.

Natural Language Processing has the following applications,

• Sentiment Analysis: NLP algorithms can analyze text to determine the sentiment expressed, providing insights into public opinion, customer feedback, and social media trends. This helps businesses make data-driven decisions and tailor their strategies accordingly. Here at Olery we use Sentiment Analysis with opeNER Project - https://www.opener-project.eu/ to extract millions of experiences from reviews in the Hospitality Sector.
• Machine Translation: NLP plays a crucial role in machine translation, making it possible to automatically translate text from one language to another. Services like Google Translate leverage NLP techniques to bridge language barriers and facilitate cross-cultural communication.
• Chatbots and Virtual Assistants: NLP powers chatbots and virtual assistants, enabling them to understand user queries, provide relevant responses, and engage in human-like conversations. These applications have transformed customer service, providing instant support and enhancing user experiences.
• Information Extraction and Text Summarisation: NLP algorithms can extract important information from large volumes of text and summarise it in a concise form. This is particularly useful in areas such as news aggregation, research, and document analysis, where efficient information retrieval is essential.

Natural Language Processing has made significant strides, it still faces several challenges:

• Ambiguity: Human language is inherently ambiguous, and words can have multiple meanings depending on the context. Resolving this ambiguity accurately remains a challenge for NLP systems.
• Cultural and Linguistic Variations: Different languages, dialects, and cultural nuances pose challenges in building robust NLP systems that can handle diverse linguistic patterns and variations.
• Named Entity Recognition: Identifying and extracting entities such as names of people, organizations, and locations accurately is a complex task, as these entities can be expressed in various forms and can differ across languages.
• Lack of Contextual Understanding: While NLP systems have made progress in understanding individual sentences, comprehending the broader context and inferring implicit meaning remains an ongoing challenge.

Natural Language Processing moving forward

As technology advances, NLP holds tremendous potential for further innovation and impact. Recent advancements in deep learning, particularly with models like GPT-3, have demonstrated remarkable capabilities in language understanding and generation. These models have opened up possibilities in areas such as content creation, automated report writing, and even creative writing.

Additionally, with the rise of multimodal AI, NLP is merging with computer vision to handle textual and visual information together. This integration enables systems to process and interpret complex data sources, leading to more comprehensive and contextually aware AI systems.

Natural Language Processing has emerged as a transformative field within AI, bridging the gap between human language and machines. Its applications are vast, ranging from sentiment analysis to machine translation, and it has significantly impacted various industries. While challenges persist, the future of NLP looks promising with ongoing advancements in deep learning and multimodal AI. As NLP continues to evolve, it will empower us to communicate

Olery Sentiment Analysis Technology has gone through a fundamental rebuild and Sentiment V2 (Fusion of Data) is our latest and most advanced Sentiment Analysis Technology. Sentiment V2 analyses millions of reviews in multiple native languages. It detects the positive and negative sentiment towards specific Hospitality related Ratings, for example - Room and Personnel. Sentiment V2, then converts the captured sentiment into an easy to understand numerical score out of 10. The Ratings are lined up with the Numeral Rating system, Cleanliness, Facilities etc. This allows the Fusion of Sentiment and Numerical data for the first time within the Hospitality Industry. Capturing deeper and more translatable information.

Sentiment Analysis Technology has traditionally been based upon identifying positive and negative words within a written text or in the Hospitality Sector, Review. It then assigns a positive percentage to the whole review. This provides a very basic understanding of the sentiment as a whole, but fails to capture anything deeper and meaningful for the Hospitality Industry.

The next evolution was to take it a step further in identifying keywords within a Topic and provide a percentage of positivity on those keywords for that Topic. This required two separate lexicons of keywords and a more sophisticated detection technology, that is based per language. You have to map out the language structure to correctly identify the correct polarity word for the Keyword linked to the Topic. Within the Hospitality Industry, this is typically done only for English and other Languages are translated into English first. Also, it only covers Accommodations. The percentage system of scoring is also difficult if not impossible to link with numerical data. Olery Sentiment V1 was an expanded version of this with 6 Language structures mapped out instead of just English.

Olery Sentiment V2

Olery has taken Sentiment Analysis to the next level with V2 (Fusion of Data). Because we have gone Vertical, we are able to design a much more translatable scoring system based upon a more precise context based Lexicon (keywords) per rating. The Vertical Approach allows us to build more relatable and specific Keywords per rating. For example, Attractions Vertical, Facilities Rating, Attractions Sub-Rating, can now be extracted from the written reviews.

A variation functionality added to our Lexicons technology, allows us to navigate the often chaotic world of Online Reviews. From Typos caused by Fat Fingers or auto correct gone wrong, the variation allows us to capture these correctly to a much bigger degree than previously. An example of this is in English, the word Personal is often mis-used to mean Personnel when talking about staff in reviews. Variation allows us to link Personal to Personnel and capture the sentiment when in previous sentiment versions, this would have been missed.

Expanded Language Detection and Structures, allows us to grow our native language sentiment Analysis capabilities to currently 14 languages and expanding. Sentiment V2 (Fusion of Data) is the only Sentiment Analysis technology within the Hospitality Industry that covers this amount of languages natively.

Fusion of the Sentiment Scores and Numerical Scores is a Sentiment First. By converting the positive and negative sentiment into an industry established scoring system, we create more usable and actionable insights than ever before. Sentiment can now be extracted from Travel Composition, Nationality and much more.

A more structured approach that Sentiment V2 (Fusion of Data) has, allows for customers to play freely and easily with their own created Ratings. You build the keywords per rating and utilise our lexicon of Polarity words to extract sentiment that is related to your needs.

The Sentiment V2 (Fusion of Data) is not just an update of the current technology, it is a complete rebuild from scratch to enable the platform to run “stand alone”. We are now capable of offering Sentiment as a Service, enabling you to analyse whatever data sets you like to be analysed (eg guest communication via emails, whatsapp, chats etc).