Introduction:
Media monitoring is a crucial tool for businesses to track mentions of their brand, competitors, and industry in the news and social media. However, with the ever-increasing volume of information published, it can be challenging for an organization to process and determine sentiment of their brand. This is where sentiment analysis comes in, which allows businesses to quickly and effectively assess the tone and context of the articles being monitored.
There are different approaches to sentiment analysis, including rules-based analysis, natural language processing (NLP) with machine learning algorithms, and manual analysis. Each approach has its own strengths and limitations, and the choice of which approach to use depends on the specific requirements and constraints of the analysis task.
Rules based sentiment analysis
Rules-based sentiment analysis is an approach to sentiment analysis that relies on a set of pre-defined rules to identify the sentiment expressed in a piece of text. These rules are typically created by domain experts or linguists who have a deep understanding of the language and the context in which it is used.
The rules used in rules-based sentiment analysis can take different forms, but they generally involve looking for certain keywords, phrases, or grammatical structures that are indicative of positive, negative, or neutral sentiment. For example, a rule might identify the use of words like “happy,” “joyful,” or “excited” as indicators of positive sentiment, while the use of words like “angry,” “frustrated,” or “disappointed” might indicate negative sentiment.
Rules-based sentiment analysis can be applied to a wide range of text data, including social media posts, product reviews, news articles, and more. While this approach has some limitations (such as the difficulty of creating rules that accurately capture the nuances of language and context), it can be a useful tool for quickly analyzing large amounts of text data and identifying trends in sentiment.
Natural language processing (NLP) with machine learning
Sentiment analysis can be determined by using natural language processing (NLP) and machine learning algorithms to identify the sentiment expressed in a piece of text. This can range from positive, neutral, or negative.
The process of determining article sentiment can be broken down into several steps:
1. Text preprocessing: Before sentiment analysis can take place, the text must be preprocessed. This involves removing any extraneous information such as website formatting, punctuation, and stop words. Stop words are commonly used words such as “the”, “and”, and “a” that do not add meaning to the text and can be safely removed.
2. Tokenization: The text is then tokenized, meaning it is split into individual words or phrases. This is an important step as it allows the algorithm to analyze the text on a granular level.
3. Part-of-speech tagging: Each word or phrase is then tagged with its part of speech (noun, verb, adjective, etc.). This is important as it allows the algorithm to understand the context in which the word is being used.
4. Sentiment scoring: Once the text has been preprocessed, tokenized, and tagged, sentiment scoring can take place. This involves assigning a score to each word or phrase, indicating its sentiment. For example, “happy” would be assigned a positive score, while “sad” would be assigned a negative score. These scores can be determined through various methods such as lexicon-based analysis or machine learning.
5. Aggregation: Finally, the scores for each word or phrase are aggregated to determine the overall sentiment of the article. This can be done by taking an average of the scores or using a more complex algorithm to determine the most dominant sentiment.
Just like rules based sentiment, NLP with machine learning sentiment is not always 100% accurate, and results can be influenced by various factors such as sarcasm, irony, and cultural context. Therefore, it is important to use sentiment analysis as a tool in conjunction with human analysis to ensure a complete understanding of the sentiment expressed in an article.
Manual tonal analysis
Manual tonal analysis involves human interpretation of the tone or sentiment expressed in a piece of text. While sentiment analysis algorithms can be effective, they are not always accurate and can miss nuances in the language that only a human can detect. Therefore, manual tonal analysis can provide a more nuanced understanding of the sentiment expressed in an article.
Manual tonal analysis is particularly useful for articles that contain sarcasm, irony, or satire, as these nuances can be difficult for an algorithm to detect. A human analyst can identify these nuances and adjust their analysis accordingly.
To perform manual tonal analysis, the analyst reads through the article and identifies the key themes and tone expressed by the author. They also look at the language used, such as adjectives and adverbs, to determine the tone. For example, if an article uses positive language such as “exciting” or “amazing”, this would indicate a positive tone. Conversely, negative language such as “disappointing” or “frustrating” would indicate a negative tone.
Additionally, the analyst may consider the context in which the article was written. For example, if the article is reporting on a company’s financial results, a negative tone may be expected if the company has experienced a decline in profits. However, if the article is reporting on a new product launch, a positive tone would be expected.
It is important to note that manual tonal analysis can be time-consuming and labor-intensive, particularly for larger volumes of articles. However, it can provide a more nuanced understanding of the sentiment expressed in an article, which can be invaluable for businesses looking to understand public opinion and sentiment around their brand or industry.
Conclusion:
Media monitoring platforms typically use either rules based or NLP with machine learning algorithms to determine sentiment for the news, blogs or social posts, because computer based analysis can provide sentiment feedback much faster and is less labor/cost intensive, than a manual human analysis. Neither computer based and human analysis will be 100% accurate, which could vary based on the perception or understanding of a topic by the reader. This is why many Media Monitoring companies will give its users the ability to manually determine the sentiment of each article. As the expert of your own business or industry, no one can understand it better than you do. Therefore, having a program that can rapidly offer a reasonably precise evaluation of article sentiment, along with features that enable users to conduct their own analysis, is advantageous.
