Our method for extracting aspects combined the Convolutional Neural Network and the language patterns described in [19]. This is the set of characteristics we were able to glean:
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Employment is an umbrella phrase that encompasses a wide range of job titles, responsibilities, and other aspects of working for a corporation—having "great people" and "stable and secure employment" as two of the best things about my job are only one example.
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Employees/Colleagues, in a nutshell, reveals the calibre of the company's staff and the quality of its relationships with its workers. This is epitomized by a successful company that has fostered a high-performance culture and attracted a pool of skilled workers.
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Work hours and patterns may be deduced from the positive company culture, as seen by the highly generous vacation and time off policies and the regular sabbaticals for full-time staff.
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Management, an in-depth look at the company's highest echelons; "Great place to work," for instance. A management style that takes into account the needs of all employees. Fantastic accomplishment.
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The customs and traditions of an organization are sometimes referred to as the "workplace culture" to simplify the discussion. Care is taken with the procedures, policies, culture, and staff—enormous benefits.
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"Competitive salary, Nice location, Full freedom" are just a few comments on the company's location that can be found under "Location."
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In this context, "job life" refers specifically to the type and quality of one's employment, as well as the work-life balance given by the organization, such as a friendly working atmosphere, convenient location, and exciting duties.
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Compensation, whereby pay-related remarks like "Wage is Fine" provide an in-depth look at the salary ranges offered by the organization. Great perk...until fights break out over who gets the most food. Much time is spent relaxing (which results in a lack of inventiveness).
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Salary, bonuses, and other benefits that are appropriate for this position are detailed below. These perks also change depending on one's level of seniority inside the organization. Vacation time, stock options, and a fitness centre membership with no cost to the employee is just some of the perks given.
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An excellent workplace has numerous components, including management that cares about workers' careers and a friendly atmosphere.
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The term "employee experience" refers to the perspectives of employees of varying skill levels and the range of experiences that might be received via working for the company, including but not limited to: It may be difficult for new hires, regardless of their degree of experience, to have their opinions taken seriously in a large organization.
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There is an official staff area where problems with hiring, firing, and retention may be addressed.
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Training received while working is known as "on-the-job training," often provided by an employer. For instance, you may need to adjust your schedule or be unavailable for a specific time. subpar schooling
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Because it is a complex company and constantly evolving, success takes time to realize. Personal and professional growth possibilities. Organizational growth.
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Effectiveness in Leadership, which examines the level of motivation and efficiency shown by top-level officials and the organization's leadership.
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Leadership is a study of influential leaders and their functions. Management has no idea how to make the most of its seasoned staff.
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Others who want power inspire those who desire it via their relationships with one another in politics.
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For instance, an inquiry into the firm's business may be inferred from the statement "Business is thriving at the company," which alludes to the success and trade of the company.
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Predictions about one's professional development and the future of one's organization, such as plans to become global in two years.
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In this context, "vacation" refers to the number of paid days off that the company provides the workers, as in "Paid vacation during the summer." The ability to freely traverse international boundaries.
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You may tell how supportive an organization is by observing its members' interactions with one another, such as how supervisors care for their direct reports.
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A company's dedication to a flexible work environment may be gauged by how much remote work is an option for workers.
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The performance of an employee is defined as their activities in the workplace, whether it be the quality and quantity of their labour or the demonstration of exceptional talents.
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Respect on the job is how well employees think their managers and colleagues are doing their jobs.
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Projects at work, corporate projects, products, and corporate strategies are covered; for instance, the company provides a broad range of products and a substantial number of projects.
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The strength of the market in which a business works is reflected in the firm's ability to succeed there.
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This section examines the firm's technical underpinnings, including that most of the firm's equipment is built on obsolete systems.
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Most machines, for instance, are malfunctioning, drawing attention to the fact that the company must overcome operational, regulatory, and internal obstacles.
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Both required and elective skills for the organization are outlined by the knowledge scope. Examples include "This process requires expert expertise," etc.
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Employee communication is the two-way flow of information between employees and their respective employers. Saying something like, "People at this company are extremely engaged," is an example of effective internal communication.
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Stress in the workplace, economic factors (such as low salaries), and the upsides (increased productivity) of pressure in a demanding business environment.
4.3. Contradiction Assignment for the Features
In this section, we'll examine how the polarity of each variable is established. Conditional Modifiers That Can Be Used Anywhere Our final objective is to find the universal dependencies of the included features to calculate the polarity score for each of the 30 most significant elements of a review. Our main study focuses on the interplay between adjective modifiers, adverbial modifiers, and nominal subjects (subj). To help, we provide snippets of the reviews included in our dataset. These images attempt to demonstrate the interdependence of these parts.
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Many opportunities to move forward in one's field. Plenty of possibilities. They have conservative political views. Extremely out of date and rooted in the past. Adv med (political, Very)
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Meeting interesting new individuals is an excellent advantage of work trips. Subj (travelling, perks)
The StanfordCoreNLP Parser is the software we use to discover these shared features. Once a causal relationship has been established, its valence may be determined by examining the "trigger" terms. This aspect's mood is determined by the polarity of the modifiers. Therefore we look them up in our made-up sentiment dictionary. The contradictions upon which the aspect score is based provide a reliable assessment of the property in question. This is done for the 30 highest weighted criteria in the evaluation.
Dialects and their Variations The ensemble advances to the next stage when the trigger word is not included in their emotional vocabulary. Here, we'll take a step back and examine the larger picture (window size − 5 terms used, including the aspect). We utilize the dependent patterns mentioned in [20] as a basis for determining an overall polarity score for the aspect. In our view, highly polar terms in the context will increase the polarity of the aspect as a whole. The disadvantages are now advantages, and they can be dealt with appropriately. A polarity value may be assigned to anything using ELM data. If none of these alternative techniques provides a suitable grade for the component, we shall resort to the result reached by our ELM model ( 1 - positive, 0 - negative). Using the results from the ELM, we change the aspect word by first looking it up in the sentiment dictionary and then using the following algorithm.
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)
Here, e out is the expected output of the ELM for the review, and lookup(aspect) is the polarity score searched up in the sentiment dictionary for the aspect term. Randomized ELM score when an aspect word is not included in the sentiment dictionary, a polarity value is arbitrarily generated using the ELM's output. The formula for the random score is as follows:
![](data:image/png;base64,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)
To generate random real numbers between [a,b], the rand(a,b) function uses a random number generator. It was only in 2% of the occasions where we had to employ a random number generator to assign weights to the various characteristics. These findings demonstrate that the semi-random production of aspect polarities did not affect the overall aspect-sentiment embeddings. Still, we think being fully automated is the way to go for our future endeavours. Therefore we plan to do just that.